REMOTE SENSING SYSTEMS OPTIMIZATION FOR GEOBASE ENHANCEMENT
THESIS
Steve J. Paylor, Captain, USAF
AFIT/GEE/ENV/03-20
DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY
AIR FORCE INSTITUTE OF TECHNOLOGY
Wright-Patterson Air Force Base, Ohio
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.
The views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U. S. Government.
AFIT/GEE/ENV/03-20
REMOTE SENSING SYSTEMS OPTIMIZATION FOR
GEOBASE ENHANCEMENT
THESIS
Presented to the Faculty
Department of Systems and Engineering Management
Graduate School of Engineering and Management
Air Force Institute of Technology
Air University
Air Education and Training Command
In Partial Fulfillment of the Requirements for the
Degree of Master of Science in Engineering and Environmental Management
Steve J. Paylor, B.S.
Captain, USAF
March 2003
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.
AFIT/GEE/ENV/03-20
REMOTE SENSING SYSTEMS OPTIMIZATION FOR
GEOBASE ENHANCEMENT
Steve J. Paylor, BS Captain, USAF
Approved: __//Signed______________________________________ 14 Mar 03__ Heidi S. Brothers, Lt Col, USAF (Chairman) date __//Signed______________________________________ __03 Mar 03__ Dr. Robert C. Frohn, University of Cincinnati (Member) date __//Signed______________________________________ __21 Feb 03__ Karl S. Mathias, Lt Col, USAF (Member) date __//Signed______________________________________ __21 Feb 03__ David L. Bignell, NAIC (Member) date
vii
Acknowledgments
I would like to express my gratitude to Lt Col Heidi Brothers for her guidance and
enthusiasm throughout this research effort. I would also like to extend special thanks to
Col Brian Cullis who generously supported this research and whose briefing at AFIT
inspired me to explore GeoBase and remote sensing opportunities.
I am especially grateful to Dr. Robert Frohn, who took time from his teaching and
research at the University of Cincinnati, to lend expertise and validation to this work.
Thanks to Mr. David Bignell for his remote sensing experience and assistance in
obtaining imagery from government sources. I am also appreciative of the software
recommendations made by Lt Col Carl Mathias, and GeoBase laboratory assistance
provided by Lt Col David Biros. I would like to express sincere thanks to Mr. Danny
Portillo, TSgt Delbert Brown, and Major Anthony Castaneda, who tested the imagery
decision tools and provided valuable feedback.
I would like to thank my wife for encouraging me and adding fun and humor to
the past 18 months. You were my in-house thesis advisor, who motivated me to stay on
track and strive to do my best.
Steve Paylor
viii
Table of Contents Page
Acknowledgments ........................................................................................................... vii
List of Figures.................................................................................................................... x
List of Tables .................................................................................................................... ix
Abstract.............................................................................................................................. x
I. Introduction ............................................................................................................... 1-1 1.0 BACKGROUND......................................................................................................... 1-1 1.1 PROBLEM STATEMENT AND CONTEXT .................................................................... 1-5 1.2 RESEARCH OBJECTIVES .......................................................................................... 1-5 1.3 METHODOLOGY ...................................................................................................... 1-5 1.4 RELEVANCE ............................................................................................................ 1-6 1.5 THESIS OVERVIEW .................................................................................................. 1-7
II. Literature Review ..................................................................................................... 2-1 2.0 INTRODUCTION ....................................................................................................... 2-1 2.1 GEOBASE STATUS................................................................................................... 2-2 2.2 REMOTE SENSING ................................................................................................... 2-7 2.3 PHOTOGRAPHIC IMAGES ......................................................................................... 2-8
2.3.1 Black and White Panchromatic Photographs............................................... 2-11 2.3.2 Black and White Infrared Photographs ........................................................ 2-15 2.3.3 Color Photographs ....................................................................................... 2-18 2.3.4 Color Infrared Photographs ......................................................................... 2-19 2.3.5 Photography Availability .............................................................................. 2-22 2.3.6 Photography Cost ......................................................................................... 2-24 2.3.7 Photography Limitations .............................................................................. 2-24
2.4 DIGITAL IMAGES................................................................................................... 2-25 2.4.1 Multi-spectral Digital Image ........................................................................ 2-25 2.4.2 Radar Images ................................................................................................ 2-46 2.4.3 LiDAR Images............................................................................................... 2-53 2.4.4 Digital Image Availability............................................................................. 2-60 2.4.5 Digital Image Cost........................................................................................ 2-62 2.4.6 Digital Image Limitations ............................................................................. 2-62
III. Methodology ............................................................................................................ 3-1
3.0 INTRODUCTION ....................................................................................................... 3-1 3.1 GEOBASE AND GIS LEARNING ............................................................................... 3-1
3.1.1 GeoBase Laboratory....................................................................................... 3-2 3.1.2 GIS Training ................................................................................................... 3-3
3.2 AIR FORCE BASE APPLICATIONS LIST..................................................................... 3-4 3.2.1 Options to Determine Air Force Applications List ......................................... 3-4 3.2.2 Air Force Applications List Decision ............................................................. 3-5
3.3 ANALYSIS ............................................................................................................... 3-6 3.3.1 Potential Analysis Methods............................................................................. 3-6
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Page
3.3.2 Analysis Method Outlined............................................................................... 3-8 3.4 VALIDATION ......................................................................................................... 3-13
3.4.1 Potential Methods of Validation ................................................................... 3-13 3.4.2. Validation Method Chosen- -Researcher Controlled Limited Field Test.... 3-14
IV. Analysis and Validation .......................................................................................... 4-1 4.0 INTRODUCTION ....................................................................................................... 4-1 4.1 GEOBASE AND GIS LEARNING ............................................................................... 4-1
4.1.1 GeoBase Laboratory....................................................................................... 4-1 4.1.2 GIS Training ................................................................................................... 4-2
4.2 AIR FORCE BASE APPLICATIONS LIST..................................................................... 4-4 4.3 ANALYSIS ............................................................................................................... 4-9
4.3.1 Analysis Method............................................................................................ 4-10 4.3.2 Jensen and Cowen Table and Diagram Foundation .................................... 4-10 4.3.3 Imagery Decision Matrix - Initial ................................................................. 4-14 4.3.4 Imagery Decision Matrix - Final .................................................................. 4-19 4.3.5 Imagery System Key - Initial......................................................................... 4-23 4.3.6 Imagery System Key - Final.......................................................................... 4-27
4.4 VALIDATION ......................................................................................................... 4-30 4.4.1 United States Air Force Academy, Colorado ............................................... 4-31 4.4.2 Elmendorf Air Force Base, Alaska ............................................................... 4-32 4.4.3 Wright-Patterson Air Force Base, Ohio ....................................................... 4-33 4.4.4 Imagery Procurement ................................................................................... 4-34 4.4.5 Imagery Decision Flow Chart ...................................................................... 4-39
V. Conclusions ................................................................................................................ 5-1 5.0 INTRODUCTION ....................................................................................................... 5-1 5.1 RESULTS ................................................................................................................. 5-1 5.2 SUMMARY............................................................................................................... 5-2 5.3 LIMITATIONS........................................................................................................... 5-4 5.4 FUTURE RESEARCH................................................................................................. 5-5
Appendix A: Initial Imagery Decision Matrix ........................................................... A-1
Appendix B: Final Imagery Decision Matrix ............................................................. B-1
Appendix C: Initial Imagery System Key................................................................... C-1
Appendix D: Final Imagery System Key .................................................................... D-1
Appendix E: Instruction Sheet .................................................................................... E-1
Appendix F: Imagery Decision Flowchart...................................................................F-1
List of Symbols, Abbreviations and Acronyms..........................................................G-1
Bibliography .............................................................................................................. BIB-1
Vita .............................................................................................................................VIT-1
x
List of Figures
Page
Figure 2-1: Garrison GeoBase Resources........................................................................ 2-3
Figure 2-2: GeoBase CIP ................................................................................................. 2-4
Figure 2-3: GeoBase CIP ................................................................................................. 2-5
Figure 2-4: GeoBase CIP ................................................................................................. 2-6
Figure 2-5: Electromagnetic Spectrum ............................................................................ 2-8
Figure 2-6: Spatial Resolution Harrisburg Scale = 1:100,000....................................... 2-10
Figure 2-7: Urban Harrisburg 1:4,000 ........................................................................... 2-11
Figure 2-8: Ultraviolet ................................................................................................... 2-12
Figure 2-9: Visible ......................................................................................................... 2-12
Figure 2-10: Infrared...................................................................................................... 2-16
Figure 2-11: Spectral Reflectance of Vegetation.......................................................... 2-17
Figure 2-12: Color and Color-Infrared .......................................................................... 2-18
Figure 2-13: Color vs. Color-Infrared Photograph ........................................................ 2-21
Figure 2-14: Landsat-7 ETM+....................................................................................... 2-29
Figure 2-15: Quickbird Multi-Spectral Image ............................................................... 2-30
Figure 2-16: Green Island, Kuwait ................................................................................ 2-31
Figure 2-17: 1-Meter Panchromatic............................................................................... 2-31
Figure 2-18: 4-Meter Multi-Spectral ............................................................................. 2-32
Figure 2-19: NASA ATLAS On-Demand Aerial Imagery Platform............................. 2-35
Figure 2-20: Pixels......................................................................................................... 2-36
Figure 2-21: Spatial Resolution Comparison................................................................. 2-39
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Figure 2-22: Bird Strike Hazard to C-5 ......................................................................... 2-41
Figure 2-23: Summer Density Map ............................................................................... 2-42
Figure 2-24: Winter Density Map.................................................................................. 2-42
Figure 2-25: Bird Avoidance Model.............................................................................. 2-43
Figure 2-26: Thermal Plume.......................................................................................... 2-46
Figure 2-27 Microwave Wavelengths............................................................................ 2-48
Figure 2-28: ERS-1 Alaska Forest Fire Scarring........................................................... 2-49
Figure 2-29: Radar Images............................................................................................. 2-52
Figure 2-30: LiDAR Contour Image.............................................................................. 2-55
Figure 2-31: LiDAR Vegetation Capture ...................................................................... 2-56
Figure 2-32: LiDAR Attribute Data............................................................................... 2-57
Figure 2-33: LiDAR Hydrology Modeling.................................................................... 2-57
Figure 2-34: LiDAR Simulation .................................................................................... 2-58
Figure 2-35: LiDAR Simulation .................................................................................... 2-59
Figure 2-36: Three-Dimensional Image......................................................................... 2-59
Figure 4-2: IKONOS 1-Meter Black and White Panchromatic..................................... 4-37
Figure 4-4: IKONOS 4-Meter Near-Infrared Band from Multi-Spectral Imagery........ 4-38
Figure E-1: Spatial Resolution of Features......................................................................E-2
Figure E-2: Spatial Resolution of Imagery System .........................................................E-5
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List of Tables
Page
Table 2-1: Spatial Resolution of Aircraft Multi-Spectral Imagery ............................... 2-35
Table 3-1: Urban/Suburban Attributes and Resolution. ................................................ 3-11
Table 3-2: Spectral Legend............................................................................................ 3-12
Table 3-3 USGS Level I-V. ........................................................................................... 3-12
Table 4-1: Air Force Applications List Examples ........................................................... 4-5
Table 4-2: GeoBase Application Examples.................................................................... 4-6
Table 4-3: Applications.................................................................................................. 4-15
Table 4-4: Minimum Resolution Requirements ............................................................ 4-16
Table 4-5: Imagery System............................................................................................ 4-17
Table 4-6: Notes............................................................................................................. 4-18
Table 4-7: Applications Column.................................................................................... 4-21
Table 4-8: Minimum Temporal, Spatial, Spectral Resolution; and Imagery Systems .. 4-22
Table 4-9: Spectral Legend from Imagery System Key ................................................ 4-24
Table 4-10: Imagery System, Revisit Temporal Resolution, and Spatial Resolution ... 4-26
Table 4-11: Spectral Resolution, Availability, and Qualitative Cost............................. 4-27
Table 4-12: Imagery Category, Imagery System, Temporal Resolution, and Spatial
Resolution .............................................................................................................. 4-29
Table 4-13: Spectral Resolution, Availability, Cost and General Use .......................... 4-30
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AFIT/GEE/ENV/03-20
Abstract
The U.S. Air Force is in the process of implementing GeoBase, a geographic
information system (GIS), throughout its worldwide installations. Air Force GIS needs
can be augmented by imagery from aerial and satellite platforms. Imagery has greatly
improved over the past several years and provides high resolution coverage of features on
earth. Various imagery types will significantly increase GeoBase usefulness in a range of
mission requirements. Potential Air Force uses of imagery include identifying heat loss,
environmental monitoring, command decision-making, and emergency response.
The research develops a decision tool to determine the appropriate imagery for a
given Air Force Application. Current literature identified proven imagery applications.
Literature review and a 2002 Air Force Geo-Integration Office (AF/GIO) survey were
used to develop a comprehensive imagery applications list that satisfies Air Force
mission requirements. An imagery decision matrix was crafted that allows a user to
select an application and see imagery that fulfills the requirements for the task. An
imagery system key provides further details of each imagery type.
The matrix was tested at three Air Force bases. Increased awareness of the
possibilities of an imagery-enriched GeoBase, and the efficiency afforded by the matrix,
greatly reduces the time to identify and implement imagery. Available imagery was
identified for the three Air Force bases at the National Imagery and Mapping Agency
(NIMA) through a government contract at no additional cost. Current IKONOS imagery
of Elmendorf Air Force base was obtained for analysis and implementation into
GeoBase.
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REMOTE SENSING SYSTEMS OPTIMIZATION FOR GEOBASE ENHANCEMENT
I. Introduction
1.0 Background
“GeoBase is an initiative to change how geo-spatial information resources are
being acquired, implemented, exploited, and sustained on USAF installations around the
world” (www.geobase.org, 2001). Geo-spatial information consists of natural and
manmade features with geographic coordinates. Some examples of geo-spatial
information include a building, road, pipeline, lake, forest, and wetland identified
according to a reference system. With a single reference system in place, these features
are analyzed with respect to one another. Squadrons such as civil engineering, security
forces, and communications use this geo-spatial database to make more informed
decisions.
The GeoBase program is focused on providing a Common Installation Picture
(CIP) for all users on a given Air Force base. A CIP involves all organizations of a base
using one shared map, instead of each unit developing its own map. Personnel from
different units coordinate efforts to contribute details for one base map. This concept is
new because previously each organization developed its own map. A CIP improves
efficiency by preventing personnel from going through the costly and time-consuming
process of creating individual maps. The result of this unified effort is a more consistent
and comprehensive map product.
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The CIP is comprised of several types of data including images. At the heart of
the CIP is an image of the geographic area under consideration. Presently, the images
most often used are 1-meter black and white panchromatic aerial photography (Cullis,
2002). According to Lillesand and Kiefer, panchromatic film is sensitive to light in the
visible spectrum from 0.3 µm to 0.5 µm, representative of the human eye (1994:77).
Photography is widely available for most geographic areas and relatively inexpensive.
The core computer system that enables GeoBase users to manage geo-spatial data
is a Geographic Information System (GIS). A GIS is a computer database that contains
geo-spatial information. It is the principal tool used to input, view, and manipulate data
about a specific location or earth feature. This database of information is often casually
referred to as a map. Many layers of data can be viewed simultaneously in order to make
a more informed decision about a specific location or land feature. For example, an
image such as a panchromatic photograph taken from an airplane could be one layer of
data in a GIS. Additional layers may include computer-aided drawings (CAD) of
buildings, streets and utilities; special use or restricted zoning data; soil types by
geographic area; and elevation data. GeoBase may improve DoD decision-making in a
variety of arenas. It has the potential to support missions related to environmental
management, disaster response, homeland defense, land-use planning, sustainable
development, infrastructure management, and many other Air Force requirements. The
GeoBase concept involves many users with the ability to access and update information
on a geo-referenced relational database.
The GeoBase concept, when used as part of a deployment, is known as
GeoReach. GeoReach database requirements are similar to those for GeoBase, with
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additional contingency requirements. This research may be applied to GeoReach when
additional contingency requirements are taken into account.
The current status of GeoBase varies and depends on the mission and leadership
priorities of a particular base as well as the training and competency of the GeoBase
users. The Air Force Geo-Integration Office (AF/GIO) is responsible for assisting bases
in setting up their GeoBase function. Additionally, each major command (MAJCOM)
has a Geo-Integration Officer in charge of overseeing the development of GeoBase
programs in their MAJCOM. Some Air Force bases have functional GeoBase capability,
while others have only begun the process of setting up an operational GeoBase. Because
the Air Force is in the early stages of GeoBase implementation, base personnel will
benefit from research that provides guidance in the selection of imagery most suitable to
GeoBase.
Images of Earth features and resources are gathered by remote sensing techniques.
Remote sensing is the process of obtaining information about a geographic area from a
distant location using photographic and non-photographic methods. Photographs produce
images with a chemical reaction on light-sensitive film. Non-photographic images, also
referred to as digital images, are produced by electronic signals. Lillesand and Kiefer
make a key distinction, “Because the term image relates to any pictorial product, all
photographs are images. Not all images, however, are photographs” (1994:23). Remote
sensing equipment can be attached to different types of platforms, such as a building,
light pole, airplane, helicopter, or satellite. The most common method of obtaining
remote sensing data is through aerial (airplane or helicopter) or satellite platforms. These
platforms provide broad coverage of the Earth’s resources, providing imagery that is
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valuable to many users. A platform may be equipped with either photographic or digital
imaging equipment. Once these images are produced, a GIS is used to compile and
analyze them. Data from several sources is used to obtain information about features in
the GIS.
There is a wide range of remote sensing images that have been collected since the
1970s. The available imagery includes black and white panchromatic, black and white
infrared, color, color infrared, multi-spectral, radar, thermal, and light detection and
ranging (LIDAR) images. Remote sensing images span the energy spectrum consisting
of ultraviolet (UV) light, visible, infrared, thermal, radar and passive microwave
wavelengths (Lillesand and Kiefer, 1994:11). With the exception of the visible portion of
the spectrum, remote sensing images are able to detect distinctive segments of the
electromagnetic energy spectrum beyond the capability of the human eye. Aerial and
satellite remote sensing platforms collect imagery on a scale covering large areas of land.
Resolution, cost, and availability of imagery are some of the limitations that must
be taken into account when determining which types of imagery to incorporate into
GeoBase. Spatial resolution may provide detail to the centimeter with newer aerial
remote sensors. Older archived imagery provides a useful foundation of data until higher
resolution imagery becomes available. Images may be supplied at minimal cost, in some
instances, through government agencies such as the National Imagery and Mapping
Agency (NIMA), or images can be purchased from several commercial sources. The
availability of images is a limiting factor, since imagery is not captured for all areas of
the earth at all times. A common satellite platform, Landsat, typically passes over the
same geographic location once every two weeks. Because it captures images optically,
1-5
poor weather in the geographic location of interest may prevent a clear image of features
on the ground (Lillesand and Kiefer, 1994:431). Newer satellite platforms such as
IKONOS and Quickbird pass over the same geographic area every few days. An archive
of imagery for a specific geographic location over several months or years may be
required to gather necessary information for a project. These factors will guide the
selection of appropriate imagery to use when constructing a GeoBase database.
1.1 Problem Statement and Context
The Air Force needs to identify what type of remote sensing imagery will
optimize GeoBase capability to meet mission requirements. The Air Force is in the early
stages of GeoBase implementation throughout its bases. Each base has a unique set of
factors to take into account. Ideally, each base will consider its opportunities and
constraints before proceeding with the implementation of GeoBase. This research will
enable bases in the planning phase, and bases choosing to update data in their GeoBase,
to select the best possible remote sensing images to support mission requirements.
1.2 Research Objectives
Develop a decision analysis tool for evaluating the use of remote sensing imagery
types for specific Air Force applications. Provide a decision analysis methodology and
research matrix to the Air Force for use in future evaluations of remote sensing imagery.
1.3 Methodology
The research methodology involves a five-step process. The researcher gained
GeoBase and GIS expertise, developed a GeoBase potential-use template (Air Force
Applications List), researched remote sensing imagery, developed a decision analysis
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tool, validated the decision analysis tool, and procured imagery from a government
agency. GeoBase and GIS expertise was gained from literature review and laboratory
training on GIS Internet tutorials. A GeoBase potential-use template for a generic base
was developed from an AF/GIO study and base mission information. Current remote
sensing imagery was researched to explore applications, limits and cost of imagery. A
decision analysis tool was developed to evaluate remote sensing alternatives for Air
Force requirements. Three Air Force Base GIOs validated the decision analysis tool, and
the researcher validated imagery procurement.
1.4 Relevance
GeoBase has the potential to revolutionize the use of geospatial information in Air
Force decision-making to the extent that the Internet has made information available to
each person on a desktop computer. Expanding the capability of GeoBase with varied
imagery types to solve multifaceted problems has a direct impact on mission
accomplishment. This thesis will help to refine GeoBase as a tool essential in meeting
diverse Air Force requirements. The decision analysis tool used in this thesis provides
clear identification of which imagery is the most useful and cost effective to aid in a
problem-solving strategy. This research will promote a more compatible and free-
flowing information exchange among GeoBase users and facilitate greater efficiency and
familiarity with GeoBase. This thesis provides direction to Air Force GeoBase system
administrators when developing their GeoBase programs and empowers Air Force
leaders to make more informed decisions.
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1.5 Thesis Overview
The thesis covers literature review in Chapter 2. Archived and current imagery
capabilities are reviewed and remote sensing applications are identified. Chapter 3
outlines the methodology of this research. Decision analysis methods are explained and
parameters of this research effort are discussed. Chapter 4 provides the results and
analysis of the thesis effort. Chapter 5 provides results, a summary of the work,
limitations of the thesis, and recommendations for future research.
2-1
II. Literature Review
2.0 Introduction
This literature review describes the current status of GeoBase and remote sensing
imagery. Remote sensing produces visual information products such as photographs,
which are then used in GeoBase for analysis. The geo-spatial database that comprises
GeoBase technology displays the geographic location of natural and manmade features
according to a common reference system. A reference system shows the relative position
of features with respect to latitude, longitude, and elevation. In a GIS, this is referred to
as geo-referenced data. By showing a birds-eye view of a given feature, such as a house
and its surroundings (i.e. streets, fire hydrants, utilities, and forests), the imagery serves
as a powerful tool for analysts to better understand their environment and make more
informed decisions.
Aircraft and satellites gather remote sensing imagery. Traditionally, photographs
of the Earth were taken from airplanes. An archive of photographs dating to the 1940s is
available from the United States Geological Survey (Lillesand and Kiefer, 1994:170).
USGS routinely takes new aerial photographs of the entire United States every five to
seven years (USGS Products and Publications, 2002). Analysts can detect changes in the
landscape by comparing photographs from different dates. Photography is increasingly
being replaced by digital (electronic derived) imagery. The term imagery is used
generally to include photographs as well as digital images. Satellite imagery of the earth
has been collected and archived since the 1970s (Jensen, 2000:184-186). Newer satellites
make multiple orbits of the earth, providing the capability to cover any point on Earth
every several days. Remote sensing instruments, which measure electromagnetic
2-2
radiation reflected by Earth features, are ideal for viewing and monitoring large land
areas.
The discussion of remote sensing imagery covers photographic images including
black and white panchromatic, black and white infrared, color, and color infrared. The
review of digital imagery includes multi-spectral, radar, and Light Detection And
Ranging (LiDAR). Each section includes information on characteristics and potential
uses of the imagery. Availability, cost, and limitations are covered at the end of the
photographic and digital image sections.
2.1 GeoBase Status
The GeoBase Program involves four areas of influence. Each of these has a
specific focus depending on the needs for the GeoBase. The categories include Garrison
GeoBase, GeoReach, Expeditionary GeoBase, and Strategic GeoBase. Garrison
GeoBase covers the Air Force base-level implementation of GeoBase. GeoReach is used
in planning for forward operating locations. Expeditionary GeoBase is for deployed
locations. Strategic GeoBase provides information to senior command levels such as
MAJCOMs and Air Staff (USAF GeoBase, 2002). This research will focus on Garrison
GeoBase. For simplicity, Garrison GeoBase will be referred to as GeoBase in this
research. Figure 2-1 is an image of a typical base with infrastructure and natural
resources that will be included in the GeoBase. The infrastructure includes facilities and
utilities such as buildings, roads, waterlines, electric, sewer, and communications lines.
Natural resources included in the GeoBase to name a few are trees, water bodies, and
endangered species habitats.
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Figure 2-1: Garrison GeoBase Resources (GeoBase Overview Materials, 2003)
GeoBase coverage includes 85 main operating bases in the Continental United
States (CONUS) and outside the United States (USAF GeoBase, 2002). Because
GeoBase will provide a common framework for organizing installation data, the
information will be transferable between organizations and higher headquarters.
GeoBase makes data available from various base organizations or units. Data
contributed by base units may be in the form of Computer Aided Drawings (CAD),
imagery, or supplemental data about facilities on base. Once this data is compiled into a
single database, it is available to all base personnel. This single database makes up a
Common Installation Picture (CIP). Figure 2-2 shows the structure of a CIP. Base
organizations are indicated along the left side of the image. For example, Civil
Engineering (CE), Security Forces (SF), Wing Plans (XP), and Transportation (TRANS)
are among the units listed. Architecture, tools, and standards are used to provide a
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framework for data resources. The CIP is continually revised and updated with current
data that is accessible by all base personnel.
Figure 2-2: GeoBase CIP (USAF GeoBase, 2002)
The current GeoBase program imagery guidance specifies “a high quality image
as well as key physical and built-up infrastructure information to allow users to quickly
orient themselves to the region” (USAF GeoBase, 2002). This research will investigate
additional imagery sources that will enhance GeoBase. Figure 2-3 is an example of a
Common Installation Picture (CIP). This is a color image collected by aircraft.
Additional data on separate layers of the GIS database include facility numbers, facility
polygons shown in yellow, utility lines, main road centerlines, and street names.
This research will determine specific types of imagery that can be used in
GeoBase to provide improved capabilities for mission accomplishment. The additional
imagery will be available as one layer in a geographical information system (GIS).
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Figure 2-3: GeoBase CIP (USAF GeoBase, 2002)
Figure 2-4 is an example of a CIP and GIS user-interface. The user-interface gives the
user information regarding installation facilities along with associated attributes. The
user-interface is divided into three sections called tiles. The top tile has the CIP and a
menu indicating the active layers in the CIP display. For instance, air obstruction waivers
have a check mark next to TRAIROBS WAIVER. This layer is indicated in solid red. In
the CIP, the locations of the waiver are filled in with solid red. To remove the
appearance of the waiver from the CIP, the user mouse-clicks the check mark next to
TRAIROBS WAIVER to remove it. The data is still available, but it will not be
displayed. This categorization of data layers allows the user a more powerful tool to
access particular information from the GIS. The bottom left-tiled window indicates
additional attribute information. This information is in the form of a spreadsheet. A
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particular facility is selected by the user and highlighted in yellow. Facility A-2, an Earth
Mound, is identified as having a waiver. A ground-level image of the Earth Mound is
shown in the bottom right tile. This data helps the user to determine information from the
available data. This GIS capability can be expanded with imagery from aerial or satellite
platforms. With imagery an analyst could determine if the earth mound had vegetation
coverage without the need of a ground-level image. Near-infrared imagery would
provide this capability and is covered in Section 2.3.2, 2.3.4, and 2.4.1.
Figure 2-4: GeoBase CIP (USAF GeoBase, 2002)
GeoBase is available on a desktop personal computer and provides geospatial
information capability to existing programs such as Automated Civil Engineer System,
(ACES) and Theater Battle Management Core System-Unit Level (TBMCS-UL).
Personnel at the base level will manage GeoBase with MAJCOM Geo Integration Office
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(MAJCOM/GIO) coordination. Each Air Force base has unique requirements that will be
fulfilled at the base level. Nationwide, Air Force installations are in the process of
implementing GeoBase capabilities. Installations are on their own schedule, and some
already have an operational GeoBase (USAF GeoBase, 2002).
2.2 Remote Sensing
Remote sensing is the process of obtaining information about a geographic area
from a remote location by means of photographs and electronic sensors. This thesis uses
imagery as general terminology to include both photographs and digital images (collected
with electronic sensors). Determining the type of imagery to use for a given problem has
the potential to create a more powerful GeoBase. Efficiency is gained by demonstrating
that many problems can be solved with the same imagery. Remote sensing imagery adds
value to GeoBase for current and future needs. By identifying the appropriate use of
remote sensing products to solve Air Force problems, users are equipped with a powerful
decision-making tool.
Remote sensing can be applied to problems successfully when there is a thorough
understanding of the problem as well as the strengths and limitations of the chosen
imagery (Lillesand and Kiefer, 1994:35-36). Lillesand and Kiefer note that a favorable
outcome of remote sensing application includes many considerations. They offer several
recommendations.
…designs of successful remote sensing efforts involve, at a minimum, (1) clear definition of the problem at hand, (2) evaluation of the potential for addressing the problem with remote sensing techniques, (3) identification of the remote sensing data acquisition procedures appropriate to the task, (4) determination of the data interpretation procedures to be employed and
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the reference data needed, and (5) identification of the criteria by which the quality of information collected can be judged (1994:35).
Remote sensing makes use of the electromagnetic spectrum. Figure 2-5 is
a diagram of the electromagnetic spectrum. Wavelengths of electromagnetic
energy are indicated in meters. Because remote sensing typically uses the
ultraviolet, visible, infrared, and microwave sections of the electromagnetic
spectrum; micrometers (µm) is often used to discuss wavelengths. Images are
made that represent the ultraviolet, visible, infrared, and microwave wavelengths.
Each of these will be discussed in detail throughout Chapter 2.
Figure 2-5: Electromagnetic Spectrum (Fundamentals of Remote Sensing, 2002)
2.3 Photographic Images
Photographic systems use light sensitive film to take pictures of a geographic
area. Photographs do not involve as much complexity and expense as digital systems.
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Photographic images include black and white panchromatic, black and white infrared,
color, and color infrared film. A photograph typically has distortions in size and shape of
features. Some factors that contribute to distortion include a feature’s distance from the
center of the photograph and the angle the photograph is taken. A digital orthophoto
quadrangle (DOQ) corrects these discrepancies so users can measure true distances
directly from the image. Excellent spatial resolution is available with photographs.
Spatial resolution is the size of the smallest identifiable feature on the image. It is clearly
distinguished and separate from adjacent objects (Lillesand and Kiefer, 1994:23, 33). An
image resolution of 1-meter typically allows the analyst to see features as small as one
square meter. Features smaller than one square meter will not be recognizable.
Resolution is essential to image interpretation in order to distinguish features of interest
in an image (Lillesand and Kiefer, 1994:23).
The images in Figure 2-6 and Figure 2-7 demonstrate the concept of spatial
resolution. In Figure 2-6, the scale of the photograph is 1:100,000. This imagery is
useful for making general observations and decisions regarding a vast geographic area
and therefore would not be useful in urban applications.
Most imagery collected currently uses spatial resolution, not scale to determine
the amount of detail visible. However, scale is traditionally used when discussing the
spatial resolution of photography. Researchers calculated a value to compare
photographic scale to spatial resolution. Photograph scale can be compared to spatial
resolution with 1:100,000 scale approximately equivalent to 2.5-meter resolution. There
is also a “general linear relationship for larger scale aerial photography…”(Jensen and
Cowen, 1999:614). With this linear (proportional) relationship, it is easy to calculate that
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Figure 2-6: Spatial Resolution Harrisburg Scale = 1:100,000 (Short, 2002)
the 1:4000 scale photograph shown in Figure 2-7 has an approximate spatial resolution of
0.1-meter. This imagery is useful for many urban applications. The analyst can see cars
on the highway and a subdivision with individual homes in the image.
Smaller spatial resolution makes it easier for analysts to differentiate ground
features in an image. According to Jensen, imagery with 2-meter spatial resolution or
higher works well for interpreting urban information. Imagery at 5-meter resolution is
poor for interpreting urban information, and 10-meter or lower resolution imagery is
practically useless for urban applications (2000:16). Jensen and Cowen give an excellent
example of the spatial resolution needed to delineate a feature.
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Figure 2-7: Urban Harrisburg 1:4,000 (Short, 2002)
Another general spatial resolution rule is that there needs to be a minimum of four spatial observations (e.g., pixels) within an urban object to identify it. Stated another way, the sensor spatial resolution should be one-half the diameter of the smallest object-of-interest. For example, to identify mobile homes that are 5 m wide, the minimum spatial resolution of high quality imagery without haze or other problems is ≤ 2.5- by 2.5-m pixels (1999:614).
2.3.1 Black and White Panchromatic Photographs
Black and white photographs are typically produced with panchromatic film. The
sensor collects electromagnetic energy in the visible spectrum and produces a gray-scale
photograph. Black and white panchromatic film has a spectral sensitivity that records
electromagnetic energy from the ultraviolet and visible parts of the spectrum, 0.3µm to
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0.7µm (Lillesand and Kiefer, 1994:76-77). Figure 2-8 and Figure 2-9 indicate the
location and range of ultraviolet and visible wavelengths on the electromagnetic
spectrum.
Figure 2-8: Ultraviolet (Fundamentals of Remote Sensing, 2002)
Figure 2-9: Visible (Fundamentals of Remote Sensing, 2002)
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2.3.1.1 Characteristics
Photograph scale traditionally ranges from 1:6000 to 1:130,000. A 1:6000-scale,
means that 1” measured on the photograph equals 6,000” or 500’ in true distance on the
ground. Three levels of scale are used to categorize photographs. A small-scale image
has a scale of 1:50,000 to 1:130,000. A medium-scale image covers 1:12,000 to
1:50,000. A large-scale image encompasses 1:6000 to 1:12,000 (Lillesand and Kiefer,
1994:154-155). Large-scale images provide more detail than medium or small-scale
images.
2.3.1.2 Potential Uses
There are a multitude of uses for black and white panchromatic photography.
Urban planners use photography because of the excellent spatial resolution and full
coverage of the United States. Since photography of the United States was taken over
many years, analysts can retrieve archived photographs and compare these to more recent
images, identifying significant changes in the landscape. Panchromatic photographs have
been used for planning and urban change analysis since the 1940s (Lillesand and Kiefer,
1994:170).
For help in land-use planning, soil mapping provides information on the types of
soils in a geographic location. The United States Department of Agriculture (USDA)
uses photo mosaic soil maps with medium-scale black and white panchromatic images as
a base. These maps, available since 1957, also come in a digital file format. These maps
can be useful for rangeland planning, wildlife habitat assessment, and determining land-
use suitability to recreation and development (Lillesand and Kiefer, 1994:181-2).
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Aerial photography is used as a tool in managing forests and combating fire. By
using imagery, identification of individual tree species is possible through characteristics
such as shape, size, pattern, shadow, tone, and texture. Specifically, medium-scale
photography has been used for tree species identification. Black and white panchromatic
photographs have aided in forest management efforts (Lillesand and Kiefer, 1994:192).
Natural resource and wildlife managers identify animals with white fur by
focusing on the ultraviolet portion of the spectrum using black and white panchromatic
film. A special filter on the camera that absorbs visible energy wavelength permits the
camera to detect only the ultraviolet energy. White-coated seal pups, polar bears, arctic
foxes, and hares are easily identified using this technique. Their coats absorb the UV
energy and appear black on the photograph (Lillesand and Kiefer, 1994:78-79).
Panchromatic photographs were used in a 1995 study to evaluate land-use
changes over time. The analysts used photographs of western Phoenix, Arizona from
seven dates covering 32 years. The photography scale ranged from 1:8,000 to 1:21,000.
Landfill area decreased from 79 hectares in 1976 to 52 hectares in 1981. By comparing
photography from 1985 to photography from earlier dates, researchers determined that
many abandoned landfill sites had been converted to urban undeveloped land and public
land. The population of western Phoenix was unaware of a possible human health risk
from municipal and industrial waste disposal landfill sites. Researchers discovered, with
the aid of a Soil Conservation Service map, that soils of these former landfill sites had
poor properties for containing waste and pesticides. Several landfill sites were located
along the Salt River, which flooded in 1980 and 1993. Seepage and flooding problems
may have led to groundwater contamination. The study demonstrated that “Historical
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photography is especially useful in the location of inactive or ‘abandoned’ sites or, in
many cases, sites that have been buried and subsequently developed”(Mack et al,
1995:1017-1020). Potential human health risks were identified using historical
panchromatic photography with additional resources.
2.3.2 Black and White Infrared Photographs
Black and white infrared photography records energy from the ultraviolet (UV),
visible and near-infrared parts of the spectrum from 0.3µm to 0.9µm. This film has a
wider spectral sensitivity than black and white panchromatic film and can detect the near-
infrared spectral band that humans are incapable of seeing. The imagery product is a
more complex snapshot from which analysts are able to separate features based on near-
infrared reflectance.
2.3.2.1 Characteristics
A camera filter is used to collect only the near-infrared electromagnetic energy
wavelengths from 0.7µm to 0.9µm. The filter allows only near infrared to pass through
and create a photograph. Figure 2-10 shows the infrared section of the electromagnetic
spectrum. The near infrared is used for photography. Mid-infrared and thermal-infrared
are discussed later in the multi-spectral digital imagery section. Frequency, in hertz (Hz),
is another measure of the wavelengths in the electromagnetic spectrum. Black and white
infrared photography has an upper limit to sensing the electromagnetic spectrum because
of the “photochemical instability of emulsion materials.” The lower limit of spectral
sensitivity is due to the atmosphere and glass camera lens absorbing electromagnetic
energy as well as the effects of atmospheric scattering (Lillesand and Kiefer, 1994:77).
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Figure 2-10: Infrared (Fundamentals of Remote Sensing, 2002)
2.3.2.2 Potential Uses
Black and white infrared photography is appropriate for a wide range of
environmental applications. Some of these include forest, agricultural, rangeland,
wetland, and urban vegetation management. The management of habitats for endangered
species is possible by determining the amount and type of vegetation in a geographic
area. The spectral reflectance of the near-infrared wavelength can distinguish deciduous
trees from coniferous trees. Electromagnetic energy is reflected in varying amounts
depending on the type of tree (Jensen, 2000:126). A sensor records the reflected infrared
energy, and an image is formed. According to Lillesand and Kiefer, “On black and white
infrared photographs, deciduous trees (having higher infrared reflectance than conifers)
generally appear much lighter in tone than do conifers” (1994:15). This ability to
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distinguish deciduous from coniferous trees makes it easier to identify the amount of land
covered by each type of tree. With this information, the habitat of animals that depend on
a certain type of tree in the region can be studied. This is a significant advantage of black
and white infrared photography over black and white photography. Analysts cannot
distinguish between deciduous and conifer trees using black and white photography.
Figure 2-11 details the capability of infrared imagery to distinguish between
healthy vegetation and stressed vegetation. The spectral reflectance of healthy and
stressed vegetation is similar in the visible portion (0.4-0.7µm) of the electromagnetic
spectrum. Imagery using the infrared portion of the electromagnetic spectrum allows
analysts to discern between healthy and stressed vegetation. Healthy vegetation has a
high near-infrared (0.7-1.1µm) reflectance of electromagnetic energy, while stressed
vegetation has a low reflectance in the near-infrared region. In a near-infrared image the
healthy vegetation will have a brighter shade of gray for black and white infrared
imagery, or typically a brighter red color for multi-spectral imagery.
Figure 2-11: Spectral Reflectance of Vegetation (Remote Sensing: The Logistics, 2003)
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2.3.3 Color Photographs
Color film is used to record electromagnetic energy in blue, green, and red light
wavelengths. These visible light wavelengths range from 0.40 µm to 0.70 µm. Color
photographs allow for more distinction than black and white photographs because it is
easier for people to discern millions of color variations than just 32 tones of gray
(Lillesand and Kiefer, 1994:79, 81).
2.3.3.1 Characteristics
Color photographs are recorded using three layers of information. A yellow dye
records the blue light wavelength; magenta dye records the green light wavelength; cyan
dye records the red light wavelength (Lillesand and Kiefer, 1994:81). Figure 2-12 shows
a side-by-side comparison of a color photograph (on the left) and a color-infrared
photograph (on the right). The color photograph allows the analyst to view features in
natural colors. The color-infrared photograph will be discussed further in the next
section.
Figure 2-12: Color and Color-Infrared (Remote Sensing of Vegetation, 2002)
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2.3.3.2 Potential Uses
Color photographs are useful for land and urban management. Applications range
from traffic studies to disaster response. If color photography is available and preferred
over black and white photography, it may be used in applications where black and white
photography is used. Color photography is easier to understand because people are used
to seeing everything in color. People without training in the use of black and white
photography or infrared photography will find it easier to use color photography. The
Air Force also places a high priority on community planning, and color photography will
make compliance with architectural standards, including color and style of buildings,
easier. Prices for photography are discussed in Section 2.3.6.
Planners who are inexperienced at interpreting black and white photography may
find color photography easier to interpret. In their analysis of Phoenix, Arizona
environmental hazards, Mack et al. used available color and color-infrared photography
to supplement black and white panchromatic. The authors noted that the color
photography and additional analysis information “was a useful reference for the
recognition and interpretation of solid and liquid waste disposal sites” (Mack et al.,
1995:1017).
2.3.4 Color Infrared Photographs
Color infrared photographs provide several advantages over black and white,
black and white infrared, and color photographs. Analysts use color-infrared
photographs to identify health and abundance of vegetation, land-water boundaries,
suspended sediment and organic matter in waterways, and camouflaged resources.
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2.3.4.1 Characteristics
Color-infrared film records electromagnetic energy in the ultraviolet, blue, green,
red, and near-infrared light wavelengths from 0.3µm to 0.9µm. The infrared wavelength
from 0.7 µm to 0.9 µm provides additional capabilities over color film.
A camera filter is used to prevent ultraviolet (UV) and blue light from exposing
the film. The UV and blue light wavelengths naturally scatter in the atmosphere.
Filtering these wavelengths makes a clearer photograph. The green, red, and near-
infrared wavelengths expose the film. The electromagnetic energy is collected and
portrayed on the film in false colors to incorporate the infrared wavelengths, because
infrared is colorless. The near-infrared wavelength gives imagery analysts the ability to
separate features with varying amounts of near-infrared reflectance (Jensen, 2000:116).
2.3.4.2 Potential Uses
Figure 2-12 shows a comparison of urban color and color-infrared photographs.
With the color-infrared photograph on the right, it is easier for an analyst to identify the
health of vegetation. The healthier vegetation is bright red. Figure 2-13 shows a color
photograph on the left compared to a color-infrared photograph on the right. In the color-
infrared photograph, the area of water is black because it is devoid of suspended sediment
or organic matter. With color-infrared photography, the sensor records infrared energy
reflected off of vegetation, which appears red by convention. As shown in Figure 2-13, it
is much easier to see the boundary between water and land in the color-infrared
photograph than in the color photograph.
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Figure 2-13: Color vs. Color-Infrared Photograph (Jensen, 2000:Plate 4-3)
Waterways that have sediment or organic matter will reflect infrared energy,
making the water appear lighter in the image. By comparing color and color-infrared
photographs, an analyst can determine portions of a waterway with high amounts of
polluting sediment or organic matter. Unlike color photography, color-infrared
photography is suitable for detecting sediment and organic matter that degrades water
quality. This data can be used to perform water quality monitoring. During this process,
color-infrared photography is combined with additional geo-spatial information including
hydrology, topography, and soil data in a GIS database. The geo-spatial information
allows researchers to determine peak flow of water and soil erosion caused by storms.
This analysis may also be used by the military in formulating similar soil erosion models.
Some researchers have used 1:10,000-scale color-infrared photography and 3-meter
multi-spectral data to determine the land-cover information for a geographic area. Multi-
spectral images will be covered in a later section. With this information, researchers
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identify potential problem areas that require “vegetated buffer strips and other best
management practices” (Jensen, 2000:403-404).
During WWII, the U.S. military required a technology to detect camouflaged
targets. Researchers determined that chlorophyll in vegetation reflects infrared energy.
This phenomenon does not occur with camouflage or any other non-vegetation. The
color-infrared photograph was successfully used by the military in WWII to locate targets
covered with camouflage (Jensen, 2000:114-115).
2.3.5 Photography Availability
One of the most extensive inventories of imagery products is maintained by the
United States Geological Survey (USGS). USGS has an archive of more than eight
million aerial photographs. Black and white panchromatic, black and white infrared,
color, and color-infrared photographs are offered by the USGS. Most of these
photographs have a scale of 1:40:000. Photographs with a scale of 1:24,000 are also
available. Digital images of the photographs called digital orthophoto quadrangles
(DOQ) are also available from USGS. The distance of ground features may be measured
directly from DOQ files, because the photo is geometrically corrected to reflect true
distances. Digital orthophoto quadrangles are available in either black and white, color,
or color-infrared in a variety of formats including CD-ROMs, 8 mm tape, electronically
through file transfer protocol (FTP), and digital versatile disc-recordable (DVD-R)
(USGS Products and Publications, 2002).
USGS and other national agencies have been taking aerial photographs on a
regular basis since the 1940s, and these are available for a nominal fee. Photographs can
be ordered through the USGS website at www.usgs.gov or by calling
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1-800-ASK-USGS (257-8747).
Panoramic photography is available from the U.S. Forest Service (USFS) and the
U.S. Environmental Protection Agency (EPA). Panoramic photography provides a series
of images for an area of interest. The USFS has 1:15,840-scale photography, while
typical EPA photography is 1:6000-scale (Lillesand and Kiefer, 2000:197-199).
The National Imagery and Mapping Agency (NIMA) is a major source of aerial
photography (Commercial Imagery Copyright & License Interim Guidance, 2002).
SPIN-2 aerial photography at 1:10,000 and 1:50,000 scale, has been collected since 1981
by the Russian Space Agency (Jensen, 2000:235-238).
The U.S. Department of Agriculture Agricultural Stabilization and Conservation
Service (USDA-ASCS) have been taking aerial photographs regularly since 1937. The
scale of the photography at USDA and archived USGS photography generally is 1:20,000
(Lillesand and Kiefer, 2000:197-199).
The National Aeronautics and Space Administration (NASA) has collected
photographs at scales of 1:60000, 1:65,000, 1:120,000 and 1:130,000. (Lillesand and
Kiefer, 2000:197-199).
Private companies also provide photographs and DOQ’s as well as additional
products. Photography is available from an archive or on demand by hiring a company to
take photography. Archived photography is significantly less expensive, provides a
wealth of data about a geographic area, and covers many dates. On-demand photography
is available through many companies and can provide updated data each day for
dynamic situations such as disaster response. Some examples of private companies that
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provide on-demand aerial imagery and GIS value-added products are Leica Geosystems
and Surdex Corporation (Hoffmann, 2002).
2.3.6 Photography Cost
The cost of archived USGS aerial photograph products ranges from $10.00 for a
9” square black and white panchromatic photo to $75.00 for a 36” square color or color
infrared photograph. The 9” photograph covers approximately five square miles.
Geometrically corrected data such as the digital orthophoto quadrangle costs $37.50 to
download a copy from USGS to your computer. A digital versatile disc-recordable
(DVD-R) costs $67.50 per copy. A handling fee of $5.00 is charged for each product.
Private companies also provide special order products with prices dependent on the data
and format requested (USGS Products and Publications, 2002).
The cost of on-demand aerial imagery depends on the requirement of the user.
Planners in Martin County, Florida, estimated that to collect geo-referenced aerial
photography for a countywide GIS would cost $125,000. Planners were able to use
SPOT satellite imagery instead, at a cost of approximately $5,000. Martin county also
has the flexibility to make annual updates to the GIS, monitoring growth and land-use
changes (Florida’s Martin County develops a GIS to manage future growth, 2002).
2.3.7 Photography Limitations
A limitation of photography is that an analyst requires sufficient training to
understand and interpret images that use different parts of the electromagnetic spectrum.
Most people do not easily interpret black and white panchromatic photography. Because
we see in color, specialized training is required to understand and interpret black and
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white photography. Infrared photography is even more complex, but provides more
capabilities to the skilled analyst.
Remote sensing does not provide all the information needed to solve problems.
Supporting information such as ground sampling must be used with remote sensing
photography. Human beings must choose the correct type of photography or image to
conduct analysis. Choosing the wrong remote sensing image to solve problems or
interpreting the data incorrectly will invariably lead to errors (Jensen, 2000:7).
2.4 Digital Images
Digital images are created when electronic sensors collect data from a geographic
area and a computer generates a picture. Multi-spectral, radar, and Light Detection And
Ranging (LiDAR) are examples of electronic sensors. While multi-spectral and radar
produce two-dimensional images, LiDAR generates three-dimensional images (Lillesand
and Kiefer, 1994:23).
2.4.1 Multi-spectral Digital Image
Multiple sections of the electromagnetic spectrum are used to create a multi-
spectral image. These sections are called spectral bands. Each spectral band accounts for
either a visible, infrared, or thermal section of the electromagnetic spectrum.
Over the past 30 years there has been much advancement in imagery resolution
and the number of satellites collecting imagery. Imagery is available from many satellite
programs including Landsat, Quickbird, IKONOS, OrbView, Systeme Probatoire
d’Observation de la Terre (SPOT), Indian Remote Sensing (IRS), National Aeronautics
and Space Administration (NASA) Terra, and Russian SPIN-2. Landsat imagery has
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been collected continuously since 1972 providing an archived database. SPOT, a French
satellite program, has provided imagery since 1986. In terms of imagery spatial
resolution, SPOT has stayed one step ahead of Landsat; however, Landsat has thermal
imagery capability. The IRS satellite program has collected imagery since 1988 with
later-generation satellites providing improvements in resolution. SPIN-2 is Russian
satellite imagery that collects digital panchromatic imagery that has similar capability to
the later generation SPOT-5 panchromatic imagery. SPOT, Landsat, and IRS imagery
does not provide as high a spatial resolution as the newer imagery systems, IKONOS,
Quickbird, and OrbView. IKONOS imagery is available from 1999 to present, and
Quickbird imagery is available from 2001 to present providing some of the highest
resolution capability. OrbView later-generation satellites provide high-resolution
imagery starting in 2002 that is comparable in resolution to IKONOS and Quickbird.
NASA’s Terra satellite has captured imagery with a sensor called Advanced Spaceborne
Thermal Emission and Reflection Radiometer (ASTER) since 1999. ASTER focuses on
collecting data in many infrared bands including thermal infrared at a lower spatial
resolution than IKONOS, Quickbird, and OrbView. With the exception of Quickbird
and IKONOS, each of these satellite programs is represented by several generations of
satellites as technology has improved collecting capability. Quickbird, IKONOS, SPOT,
and IRS have plans to launch improved satellites within the next three years (Status of
Selected Current & Future Commercial Remote Sensing Satellites, 2002).
Aerial platforms are used to collect multi-spectral imagery with the Daedalus and
Advanced Thermal and Land Applications Sensor (ATLAS) multi-spectral scanners.
Daedalus has been used in 25 countries over the past 30 years (Jensen, 2000:209).
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NASA’s ATLAS is a more recent multi-spectral scanner, used in several applications in
the late 1990s (Jensen, 2000:212; Description of the ATLAS Remote Sensing System,
2002).
2.4.1.1 Characteristics
Multi-spectral digital images collect data from the ultraviolet, visible, near
infrared, and often mid-infrared and thermal-infrared wavelengths; and imagery is
available in a range of spatial and temporal resolutions. Each satellite remote sensor has
unique spectral bands that collect this data on the various wavelengths. The spatial and
temporal resolution capabilities of satellite and aerial platforms vary with newer satellite
platforms approaching the spatial and temporal resolution capability of aerial platforms
while covering a larger geographic area on a frequent basis.
The Landsat satellite platform has multiple satellites providing multi-spectral
imagery. The generations of Landsat satellites follow the numbering convention of
Landsat-1, Landsat-2, and so forth up to Landsat-7. Landsat-1, 2, and 3 have a Multi-
Spectral Scanner (MSS) that collects electromagnetic energy in the visible and near-
infrared wavelengths. The spatial resolution is 56 x 79 meters for each image. Temporal
resolution, or the time it takes for the Earth-orbiting satellite to return to the same
geographic area, is 18 days (Jensen, 2000:184-197).
Landsat-4 and Landsat-5 have both a Thematic Mapper (TM) sensor and the
Multi-spectral Scanner (MSS) (Jensen, 2000:192-197). Often the terminology of
Landsat-4, Landsat-5 and Landsat TM is used interchangeably because these later
generation satellites added the Thematic Mapper capability. The Landsat TM operates
using seven spectral bands that collect data at specific electromagnetic wavelengths.
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Visible blue, green, and red light bands are utilized, as well as the near infrared, mid-
infrared (2 spectral bands), and thermal infrared band. Landsat TM produces digital
images of the earth with a 30 x 30 meter spatial resolution. The thermal infrared band
has a spatial resolution of 120 x 120 meters. Landsat TM collects imagery of the same
geographic area every 16 days (Jensen, 2000:192-197; Lillesand and Kiefer, 1994:467-
471).
Landsat-7 Enhanced Thematic Mapper Plus (ETM+) is the latest generation
Landsat satellite. The Landsat ETM+ has a panchromatic band, multi-spectral capability
similar to Landsat-TM and an improved thermal infrared band. The spatial resolution of
each of these is 15 x 15 meter, 30 x 30 meter and 60 x 60 meter respectively. The
temporal resolution is the same as Landsat-TM, with 16 days between revisits over the
same geographic area (Jensen, 2000:197-201). Figure 2-14 is an image from Landsat-7
ETM+. Forest fires burning in South Dakota were captured the morning of July 1, 2002.
The multi-spectral image indicates fire in red and orange, burn scars in dark red, and
forest in dark green. The entire area exposed to fire is outlined in yellow (Fires in
Wyoming and South Dakota, 2002). Managers use this information to make informed
decisions concerning how to fight the forest fires and evacuate people in the area affected
by the fire and smoke.
Quickbird spatial resolution is 0.61 x 0.61 meter black and white panchromatic
and 2.5 x 2.5 meter multi-spectral (Quickbird, 2002). Quickbird has stereoscopic
capability as well. The imagery, collected in side-by-side pairs, is referred to as a
stereoscopic image. An analyst can see three-dimensional data by using stereoscopic
images. Quickbird may collect imagery of the same geographic area every one to five
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Figure 2-14: Landsat-7 ETM+ (Fires in Wyoming and South Dakota, 2002)
days dependent on latitude (Jensen, 2000:224). The Quickbird image in Figure 2-15 is a
natural-color image of the Boneyard at Davis-Monthan AFB. A panchromatic 0.61-
meter spatial resolution image was collected by satellite on August 11, 2002. Out-of-
service aircraft are shown with surrounding hard desert soil (The Boneyard, Davis-
Monthan AFB, Tucson, AZ, 2002). The natural-color image is produced by assigning
colors to the gray-scale panchromatic image with a GIS software package.
IKONOS has 1 x 1 meter panchromatic and 4 x 4 meter multi-spectral spatial
resolution. The multi-spectral bands collect data in the visible and near-infrared
wavelengths. The temporal resolution, frequency of collection over the same geographic
area, is 2.9 days at 1-meter resolution and 1.5 days at 1.5-meter resolution (IKONOS,
2002).
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Figure 2-15: Quickbird Multi-Spectral Image (The Boneyard, Davis-Monthan AFB, Tucson, AZ, 2002)
Figure 2-16 is an IKONOS panchromatic image of Green Island, Kuwait. The
artificial island covers 785,000 square meters and is connected to the mainland by a 134-
meter roadway. Also, easily seen in the 1-meter image are roadways and buildings
(IKONOS 1-meter Pan-Sharpened Image of Kuwait, 2003).
OrbView has 1 x 1 meter panchromatic and 4 x 4 meter multi-spectral spatial
resolution. Satellites OrbView-3 and OrbView-4 collect imagery with a temporal
resolution of less than three days. OrbView does not have thermal infrared capability
(Jensen, 2000: 226). Figure 2-17 is a 1-meter OrbView-3 panchromatic of Las Vegas,
Nevada. This imagery when applied to urban applications makes large structures easily
distinguished. Cars are also visible in the image.
OrbView also has a 4-meter multi-spectral capability. Figure 2-18 shows crops in
California (Image Gallery-Simulated OrbView-3 four meter multi-spectral image of
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Figure 2-16: Green Island, Kuwait (IKONOS 1-meter Pan-Sharpened Image of Kuwait, 2003)
Figure 2-17: 1-Meter Panchromatic (Image Gallery-OrbView Cities one-meter image of Las Vegas, Nevada, 2003)
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Castroville, California, 2003). Managers could use multi-spectral images such as this to
determine the health of vegetation and the amount of water used for irrigation. With this
multi-spectral imagery the healthy vegetation reflects a higher amount of infrared energy
than surrounding fields. When the imagery is processed, the infrared energy recorded is
assigned the color red, by convention. With more information about the types of crops
planted, natural resource managers can determine the amount of acreage planted. With
additional information about the amount of water needed per crop acre, a water resources
manger can determine the affect on local aquifers. This is important because often cities
use the same aquifer to provide drinking water to inhabitants.
Figure 2-18: 4-Meter Multi-Spectral (Image Gallery-Simulated OrbView-3 four meter multi-spectral image of Castroville, California, 2003)
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SPOT satellites 1, 2, 3, and 4 have a spatial resolution of 10 x10 panchromatic
and 20 x 20 multi-spectral (Status of Selected Current & Future Commercial Remote
Sensing Satellites, 2002). The temporal resolution is every 26 days. SPOT-3 imagery is
not available after 1996, having been taken out of service (Jensen, 2000: 212). Archived
imagery is available up to 1996 from SPOT-3. When SPOT-1, 2, and 4 satellites are used
at the same time, temporal resolution of one day is possible for any geographic area on
the Earth (Jensen, 2000:220). SPOT does not have thermal infrared capability (Jensen,
2000:217). SPOT-5 imagery became available in 2002, providing 2.5 x 2.5 meter
panchromatic and 10 x 10 meter multi-spectral resolution Status of Selected Current &
Future Commercial Remote Sensing Satellites, 2002). The temporal resolution is less
than three days depending on latitude. When several SPOT satellites are used together,
temporal resolution improves to one day (SPOT satellite programming: a customized
service…, 2002). Thermal infrared is not available with SPOT (Jensen, 2000:220).
IRS-1A, 1B provide multi-spectral imagery spatial resolution of 36.25 x 36.25
meter and 72.5 x 72.5 meter. The temporal resolution time is 22 days, or 11 days with
both satellites. IRS-1C, 1D provide spatial resolution of 5.8 x 5.8 meter panchromatic,
23.5 x 23.5 meter multi-spectral, and 70 x 70 meter mid-infrared. The temporal
resolution is 24 days. IRS does not have thermal infrared capability (Jensen, 2000:220-
221).
The Terra satellite, a joint cooperation between NASA and Japan’s Ministry of
International Trade and Industry, has the ASTER multi-spectral sensor. ASTER collects
multi-spectral data on the visible through thermal infrared wavelengths of the
electromagnetic spectrum. ASTER has three separate instruments that collect various
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portions of the spectrum. The imagery spatial and spectral resolution is 15 x 15 meter
visible and near infrared (in stereoscopic coverage for obtaining elevations), 30 x 30
meter near and mid-infrared, and 90 x 90 meter thermal infrared (Jensen, 2000:221-222).
The temporal resolution for ASTER is 4 to 16 days (ASTER: Suez Canal, 2002).
SPIN-2 Russian satellite imagery provides panchromatic spectral resolution in
both 2-meter and 10-meter spatial resolutions. The 2-meter is available in panoramic
view. The temporal resolution is 45 days (Jensen, 2000:235-238).
Aerial multi-spectral sensors provide the high spatial resolution and imagery on
demand capability when weather is good such that clouds and precipitation do not
interfere with collection. The gap between satellite and aerial capability is quickly
closing as the newer satellites obtain spatial and temporal resolutions that are nearly as
good as aerial collection. Two aerial multi-spectral sensors are discussed in greater
detail.
The Daedalus multi-spectral scanner is available in three generations having been
used for the past 30 years. All three provide imagery capability covering the ultraviolet
to thermal-infrared wavelengths. Spatial resolution depends on the altitude of the aircraft
during imagery collection. The altitude is determined based on the spatial resolution
needs of the user and the size of the geographic area to be imaged. There is a trade-off
between the spatial resolution and the amount of time needed to collect the imagery. The
higher spatial resolution required leads to lower flight altitude above ground level (AGL)
and more passes over a geographic area to obtain the imagery. Table 2-1 shows
examples of spatial resolution possible at varying AGL. Note that the AGL is given in
meters (Jensen, 2000:209-212).
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Table 2-1: Spatial Resolution of Aircraft Multi-Spectral Imagery (Jensen, 212)
Flight Altitude AGL (m) Pixel Size (m) 1000 2.5 2000 5 4000 10 16000 40 50000 125
NASA’s Advanced Thermal and Land Applications Sensor (ATLAS) is a multi-
spectral sensor mounted on an aerial platform. The ATLAS collects electromagnetic
energy from the visible, near-infrared, mid-infrared, and thermal-infrared wavelengths.
The spatial resolution capability is nearly twice as high as Daedalus. For example,
ATLAS provides multi-spectral imagery with 2.5 x 2.5 m spatial resolution at 6000 ft
AGL and 25 x 25 m at 41000 ft AGL. As with Daedalus, ATLAS gives the user on-
demand capability for imagery, weather permitting with a Learjet 23 aircraft (Jensen,
2000:212). Figure 2-19 is an image of the Learjet 23 used by NASA to collect imagery
with the ATLAS imagery system.
Figure 2-19: NASA ATLAS On-Demand Aerial Imagery Platform (Description of the ATLAS Remote Sensing System, 2002)
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The importance of high spatial resolution can be demonstrated by comparing
IKONOS to Landsat. An image with a higher resolution such as 1 x 1 meter (IKONOS)
has more pixels of the same geographic area covered by only one Landsat pixel. The
same 30 x 30 single Landsat pixel would be represented by 900 (30 x 30=900) IKONOS
pixels. This provides more spatial resolution detail to the analyst. Figure 2-20 is part of
a black and white digital image. The image has been magnified to show the pixels that
comprise the image. This unusually high magnification demonstrates the makeup of a
digital image.
Figure 2-20: Pixels (Digital Image Processing, 2002)
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Each pixel has a gray tone value from 0 to 255 based on the reflectance of
electromagnetic energy from earth features. In this image, the white areas correspond to
a gravel pit that has high reflectance of electromagnetic energy. The dark pixels
correspond to a stream valley that has low energy reflectance (Digital Image Processing,
2002). The pixels that comprise the image in Figure 2-20 have corresponding gray tone
values. For instance, in the upper right corner, there is a gray tone value of 136, roughly
half way between 0 and 255. The pixel is medium-gray. Three rows down from 136 is
value 98. The corresponding pixel in the image is much darker gray. About two-thirds
down the same column is value 255. This corresponds to a white pixel in the image. The
reflectance recorded in each pixel value makes a composite image of a feature. Features
are distinguished based on their values (shades of gray). Multi-spectral images are
created similarly, but the reflectance values can be recorded in variations of one of three
colors: blue, green and red.
2.4.1.2 Potential Uses
The application capability of multi-spectral digital imagery is the broadest of any
of the imagery techniques. With a wide range of the electromagnetic spectrum collected,
more applications are possible with multi-spectral imagery. Multi-spectral imagery can
be used for any of the requirements of panchromatic, visible, or infrared imagery when
the appropriate spatial and temporal resolution is met by the remote sensing platform.
Most of the research to date has used earlier generation Landsat TM imagery. As
later generation, more capable satellite imagery becomes available such as IKONOS,
Quickbird, and OrbView; researchers will use these to obtain more precise information.
The possibilities for determining new information are greater because the newer imagery
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has greater resolution capability. The examples provided later in this section may be
accomplished with higher resolution imagery available from newer satellites and aerial
sensors where the spectral and temporal resolution is improved.
Landsat TM data (30-meter spatial resolution) is used to determine land-cover
classification of urban areas. However, spatial resolution is often a problem, prohibiting
the visibility of some features. Thirty-meter resolution alone is not adequate for urban
analysis. The low pixel resolution creates sub-pixel mixing due to the small spatial size
of surface objects. Sub-pixel mixing leads to a pixel color value that is a mixture of the
various objects in the 30 x 30 meter area. Objects with the highest spectral reflectance
will dominate the 30 x 30 meter area, defining the pixel value. Objects can be
misclassified due to their spectral reflectance characteristics (Stefanov et al, 2001:174).
Figure 2-21 demonstrates what happens when the spatial resolution is inadequate for the
features of interest. The image on the left is the color-infrared aerial photography with
0.5-meter spatial resolution. Using a 30-meter spatial resolution sensor, such as Landsat
TM, the image on the right would be produced. The image on the right is a composite of
all the features with one pixel color, or sub-pixel mixing. This demonstrates the need for
appropriate spatial resolution for the features of interest.
When imagery has spatial resolution that is insufficient for a particular use,
surveys (supplemental field research) can improve accuracy of the pixel classification.
One additional source of data is referred to as land-use. This type of research is readily
obtained from government agencies. Land-use may be classified as agricultural, urban,
commercial, rangeland, wetland, tundra, as well as other designations. Since Landsat has
amassed a large database of imagery since 1972, various methods for using the imagery
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Figure 2-21: Spatial Resolution Comparison (Remote Sensing of Vegetation, 2002)
in urban planning and monitoring have been developed. Additional imagery gathering
will make land-use monitoring more accurate (Stefanov et al, 2001:174).
Landsat TM imagery is also useful to measure and monitor urban sprawl (Yeh
and Li, 2001:84). An analyst can detect changes in urban sprawl by focusing on one
geographic area with the help of Landsat TM imagery. For instance, an image from 1990
and an image from 2000 can be compared to detect urban changes. Likewise, geographic
regions can be compared to identify areas that exhibit the most extensive sprawl trends.
According to Yeh and Li, “This can help government officials and planners to identify
the towns that have irrational development patterns” (2001:86).
Urban sprawl has caused environmental problems especially in rapidly growing
cities in developing countries (Yeh and Li, 2001:89). Problems include increased
sediment and chemical pollutants from new construction that affect water quality in local
streams (Bodamer, 2001:7-8). Examination of these problems, revealed by remotely
sensed imagery, could provide insight for future city development (Yeh and Li, 2001:89).
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With higher spatial resolution such as IKONOS, researchers may obtain greater precision
in measuring urban sprawl.
Multi-spectral digital images were used to model the habitat and migratory paths
of bird species. Imagery was used in a study to model potential bird strike hazards to
U.S. Air Force aircraft. From 1985 to 1993, the Air Force had about 3,200 bird strikes
each year. Bird strikes caused the loss of seven airmen, 14 jet aircraft, and over $65
million per year. To prevent further losses, researchers developed a Bird Avoidance
Model (BAM) for use by military and civilian pilots. BAM is available through the
website, http://bam.geoinsight.com (United States Bird Avoidance Model for Military
and Civilian Pilots, 2002). Imagery from the National Oceanic and Atmospheric
Administration (NOAA) was used to identify the habitat preference of turkey vultures, a
significant source of the bird strike hazard. The researchers analyzed Advanced Very
High Resolution Radiometer (AVHRR) imagery to determine vegetation types and land-
use patterns in known turkey vulture regions (DeFusco et al, 1993:1481-1487). AVHRR
imagery is a multi-spectral digital imagery that has 1.1 x 1.1 km spatial resolution and is
collected for the same geographic area every 2 days (Jensen, 2000:205). Through
imagery analysis and supplemental field research, the habitat preference and migratory
patterns of the birds were identified. With this information, Air Force flight planners can
avoid areas that are known to provide excellent habitat for the turkey vulture (DeFusco et
al, 1993:1481-1487). Flight planners input parameters of their geographic area and the
BAM provides a detailed map of potentially hazardous geographic areas. Flight planners
can use this map to plan an alternate route.
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Figure 2-22 shows the need for the BAM as birds surround a C-5 aircraft at Dover
AFB, Delaware. With the BAM, which incorporates multi-spectral imagery to model
habitat requirements and migratory patterns of birds, dangerous situations like bird strikes
can be minimized.
Figure 2-22: Bird Strike Hazard to C-5 (United States Bird Avoidance Model for Military and Civilian Pilots, 2002)
Figure 2-23 shows a color-coded relative density of the turkey vulture in the
United States during the summer from the BAM. The density was determined in part
through the use of multi-spectral imagery. The white lines on the image indicate
Department of Defense (DoD) low-level training routes. Areas of concern are where
DoD pilots train and vulture density is high.
The relative density of the turkey vulture during the winter from the BAM is
shown in Figure 2-24. Note that depending on the time of year, the density of the turkey
vulture may be higher in certain geographic areas. The BAM accounts for this by
allowing pilots to input the time of year when running the model.
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Figure 2-23: Summer Density Map (United States Bird Avoidance Model for Military and Civilian Pilots, 2002)
Figure 2-24: Winter Density Map (United States Bird Avoidance Model for Military and Civilian Pilots, 2002)
Figure 2-25 is an example of the BAM output for McChord Air Force Base and
surrounding areas. The options selected for this image are military aircrew, July 30-Aug
12 timeframe, daytime flight, and military airfields indicated. The relative risk of a bird
strike is indicated on the left side of the image. Low 1, the least risk, is indicated with the
bright green color-code and Severe 3, the highest risk, is indicated by the brown color-
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code. The map indicates areas that have a higher relative risk of bird strike. For the
model parameters selected, the geographic area surrounding McChord AFB is classified
as risk level Low 3. The areas to the upper right and lower left of the map identify risk
level Moderate 1. A pilot planning this flight route may apply the capabilities of the GIS
to avoid a potential bird strike hazard.
Figure 2-25: Bird Avoidance Model (United States Bird Avoidance Model for Military and Civilian Pilots, 2002)
Advanced sensors mounted to aircraft, similar to those used in satellites, produce
aerial multi-spectral images. Satellite images are less expensive, but aerial images via
airplane or helicopter may be necessary when adequate satellite data is not available. By
using aerial infrared images, areas of vegetation can be easily separated from developed
zones. This imagery is used to determine the percentage of vegetation, enabling analysts
to predict the amount of runoff in a high rainfall situation. Planners can use this
information to improve the storm drainage network and prevent flooding (Vincent et al,
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1996:40-41). Presently, newer satellites collect 1-meter or higher resolution imagery from
the same geographic area every few days. These images are added to the archives,
allowing analysts to access these products at a lower cost.
Landsat TM images have been successfully used to determine the amount of
water being used in an arid region. The Landsat TM sensors focus only on the infrared
wavelengths. This type of imagery is useful in the analysis of irrigated farmland,
preventing expensive surveys. Vegetation returns a bright red reflectance in the near
infrared band (Vincent et al, 1996:42-43). These images allow agricultural water
consumption to be measured. Analysts can then estimate the capacity of an aquifer in
meeting both agricultural and urban population water needs.
Thermal infrared is one section of multi-spectral range that is available on many
satellites and aerial platforms, including Landsat TM. Mounted on an aerial platform, the
Daedalus brand multi-spectral sensor has 12 spectral bands that collect ground
information. One of its spectral bands has thermal imaging capability. The sensor is
attached to an aircraft. Spatial resolution of such images varies from one to five meters
depending on aircraft altitude (Irvine et al, 1997:1583-1595).
One example illustrates the usefulness of thermal imaging in detecting buried
waste. Hazardous chemicals were part of a Department of Energy (DOE) waste site at
Oak Ridge National Laboratory. Scientists needed to identify the extent of the buried
waste trenches. The trenches were known to contain strontium-90 and cesium-137,
chemicals that radiate heat. Map records of the buried waste were destroyed in a fire in
1957, so officials used historical black and white panchromatic imagery of the trench area
to establish a general idea of where to conduct thermal mapping. Using this baseline
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imagery, a multi-spectral scanner collected a thermal infrared signature from the waste
site. The surrounding areas appeared cooler during a nighttime flight and gave the trench
area a more pronounced thermal infrared signature. The location of contaminant seepage
was identified with 1-2 meter accuracy. Successive ground sampling confirmed
contaminant seepage, and measures were put in place for remediation (Irvine et al,
1997:1583-1595).
Air Force planners may apply thermal imagery to detect hazardous waste sites
that are located on DoD land. Thermal infrared imagery may also be used to identify
heat loss from houses and steam pipes.
Figure 2-26 below demonstrates the ability of a thermal infrared sensor to detect
thermal pollution of the Savannah River in South Carolina. The river is used to provide
water for a cooling tower, with the effluent released back into the river. This image was
taken in predawn hours to easily distinguish the river temperatures from surrounding land
temperatures. The Savannah River is shown from the top left of the image. The thermal
plume from the cooling tower from the top right enters the Savannah River at higher
temperatures. White is >20°C above the river ambient temperature as seen in the top
center of the thermal plume. Red, orange, yellow, and green are 10.2-20°C, 5.2-10°C,
3.0-5.0°C, and 1.2-2.8°C, respectively, above the river ambient temperature (Jensen,
2000: 271-274).
Hyper-spectral imagery is not covered in the thesis due to time constraints. The
decision was made to focus on panchromatic and multi-spectral imagery; and exclude
hyper-spectral imagery. Hyper-spectral imagery uses hundreds of spectral bands and is
more advanced than multi-spectral imagery. Most multi-spectral imagery systems use
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Figure 2-26: Thermal Plume (Jensen, 2000:Plate 8-1)
less than a dozen spectral bands. Hyper-spectral imagery will provide additional
capabilities, but it is important for bases to understand the use of panchromatic and multi-
spectral aerial and satellite imagery first. Hyper-spectral imagery is a later generation
imagery product. Once panchromatic and multi-spectral imagery are implemented,
further gains in capability may be achieved with hyper-spectral imagery.
2.4.2 Radar Images
Radar provides the planner with remote sensing information in nearly all-weather
conditions day or night. According to Haack and Bechdol, “Radar…has the ability to
image through rain, fog, hail, smoke, and most importantly, clouds” (2000:415). Radar
provides mapping capability in geographic regions where shorter wavelengths of the
electromagnetic spectrum will not work due to poor weather. Early uses of radar
provided earth resource mapping for previously unmapped areas due to continuous cloud
cover. Most of the available imagery is from Synthetic Aperture Radar (SAR) with
aperture referring to a 1-2 meter antenna that is synthesized as a much larger antenna.
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The SAR has greater spatial resolution than a real-aperture radar (Jensen, 2000:286).
SAR imagery is available from several sources including the U.S. Space Shuttle Imaging
Radar (SIR), Canadian RADARSAT, European Space Agency (ERS-1, 2), Japanese
Earth Resource Satellite (JERS-1), Soviet Union Almaz-1, and Intermap Star 3i (Jensen,
2000:287, 324).
2.4.2.1 Characteristics
Radar is an active remote sensing tool that records reflected signals from
microwaves. In active remote sensing, microwaves are generated from the aerial or
satellite platform and sensors detect the reflected response. Figure 2-27 shows the
wavelengths of radar that are used in remote sensing. These wavelengths are much larger
than ultraviolet, visible, and infrared wavelengths. Radar remote sensing systems have
various microwave radar wavelengths that are utilized. Figure 2-27 indicates the
wavelengths corresponding to each radar band, such as P-band, L-band, S-band, C-band
and X-band.
The Shuttle Imaging Radar (SIR) has radar imagery from three different U.S.
Space Shuttle missions. These collections of radar imagery coincided with Space Shuttle
missions and each collection ended upon return to earth. The three are referred to as SIR-
A, SIR-B, and SIR-C, and had durations of 2.5 days, 8 days, and 10 days respectively. In
1978, SIR-A produced L-band wavelength radar images with 40 x 40 meter spatial
resolution. In 1984, SIR-B produced radar images also from L-band wavelength with 17
x 25 meter spatial resolution. In 1994, SIR-C produced radar images from X-band, C-
band, and L-band wavelengths each with 10-30 x 30 meter spatial resolution (Jensen,
2000:287, 319).
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Figure 2-27 Microwave Wavelengths (Fundamentals of Remote Sensing, 2002)
The RADARSAT-1 satellite was launched in 1995 and is still in operation. The
radar produces imagery with a C-band wavelength at 10-100 x 10-100 meter spatial
resolution. The satellite is in a near-polar orbit that accounts for the range in spatial
resolutions. RADARSAT-1 orbits the earth 14 times each day with temporal resolution
at 24 days. (Jensen, 2000:287, 319-322).
The ERS-1 was launched in 1991, while ERS-2 was launched in 1995. Both of
these radar-imaging satellites are still in operation. ERS-1 and ERS-2 have identical
capabilities, each using a C-band wavelength providing 30 x 26 meter spatial resolution
(Jensen, 2000:287, 322).
Figure 2-28 is an image from ERS-1 on August 23, 1991 near Bettles, Alaska.
The forest fire occurred in 1990, and the Alaska Fire Service estimated the affected area
at 116,000 hectares. The area affected by fire is seen as the bright tone in the image. The
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bright tone is created where barren ground has higher backscatter than the surrounding
forest canopy. The estimated area affected by fire was reassessed at 160,000 hectares
with the radar imagery. This value is 38% greater than the original estimate by the
Alaska Fire Service (Forest Fires, Interior Alaska, 2002). Managers can use radar to
assess the damage due to fires, other natural disasters, and clear-cutting of forests. This
can also help managers determine forested areas that may need thinned out because of too
much timber biomass.
Figure 2-28: ERS-1 Alaska Forest Fire Scarring (Forest Fires, Interior Alaska, 2002)
The JERS-1 was launched in February, 1992 and operated until October 1998.
The satellite operated in a near-polar orbit. Radar imagery was produced by L-band
wavelength at 18 x 18 meter spatial resolution (Jensen, 2000:287,322).
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The Almaz-1 was launched in 1991 and remained in operation for 18 months.
Radar imagery was produced using the S-band wavelength. The spatial resolution of the
Almaz-1 is 15 x 15-30 meter depending on the geographic location (Jensen, 2000:287,
323).
Star 3i provides radar imagery using the X-band wavelength. Spatial resolution is
3 x 3 meter and detailed digital elevation models are available with this high-resolution.
The imagery makes it possible to use radar for land-use, land-cover, and watershed
hydrologic work (Jensen, 2000:324).
The radar signature of a geographic area depends on the physical properties of the
area. An area with a smooth topographic landscape can be measured more easily than
one with a rough topographic landscape. Rough landscapes can hide or distort urban
features on a radar image (Henderson and Xia, 1997:79-83).
The advantage of using radar is its ability to penetrate cloud-covered regions. The
number of cloudy days is higher in some regions than in others. For example, the mean
annual number of cloudy days for Phoenix, Arizona is 70 compared to the Seattle-
Tacoma area with 226 days (Western Regional Climate Center, 2002). Radar is the only
remote sensing technique that can operate effectively during cloudy conditions.
Collection may be obtained at any time because radar depends on microwaves, not
electromagnetic energy. Radar can detect surface roughness, moisture content, and
electrical conductivity of features. As Jensen points out, water and moist soils and
vegetation reflect more radar energy than dryer vegetation, soil, and rocks. In radar
images, the water and moist soils will be bright with surrounding areas appearing darker
(2000:311). Radar is capable of penetrating vegetation, sand, and snow, providing
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contours of the land. Radar can also provide spatial resolution as clear as 1 x 1 meter
(Jensen, 2000:289).
2.4.2.2 Potential Uses
Radar is useful for “population estimation, assessment of the impact of human
activities on the physical environment, mapping and analyzing urban land-use patterns,
and interpretation of socioeconomic characteristics” (Henderson and Xia, 1997:79).
Population data of cities has historically been conducted through surveys that occur
usually every ten years. These surveys have a high cost in terms of time and resources.
With radar imagery an accurate and timelier assessment of population can be made
(Henderson and Xia, 1997:79-83).
Radar imaging can detect several classes of urban land-use. Radar data is often
combined with Landsat TM to obtain greater accuracy (Henderson and Xia, 1997:79-83).
Haack and Bechdol add, “By merging optical and radar, an additional portion of the
spectrum is available which may improve classification” (2000:412). Radar can
effectively enhance the data available through Landsat TM and other multi-spectral
imagery.
Radar can aid in the study of social and economic development of communities.
The distribution of urban features and the amount of open space can be obtained. By
examining radar images, the density of buildings and major transportation routes can also
be learned. An experienced radar imagery analyst can interpolate this information to
determine social and economic conditions of a city (Henderson and Xia, 1997:79-83).
By assessing the human activity of the physical landscape, planners can determine
the need for resources such as health services, and commercial expansion. Planners can
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measure urban land-use change over time and use this information to make better
decisions. Obtaining this information through traditional methods is more time
consuming and costly when planning for large cities (Henderson and Xia, 1997:79-83).
The impact of humans on the environment may be detected through use of radar
imagery. Figure 2-29 depicts part of a rainforest in Brazil where clear cutting of trees is
evident. Three wavelengths of radar, X-band, C-band, and L-band, produced the three
image examples that follow. Each radar band collects varying data about the rain forest.
The composite image at the top was generated by computer analysis. The blue and green
areas have been cleared for agriculture. The bright pink areas indicate rainforest (Jensen,
2000:315-316).
Figure 2-29: Radar Images (Jensen, 2000:Plate 9-2)
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2.4.2.3 Radar Limitations
The main limitation of radar is that images are complex and typically difficult to
interpret. According to Lillesand and Kiefer,
Microwave reflections or emissions from earth materials bear no direct relationship to their counterparts in the visible or thermal portions of the spectrum. For example, surfaces that appear ‘rough’ in the visible portion of the spectrum may be ‘smooth’ as seen by microwaves. In general, microwave responses afford us a markedly different ‘view’ of the environment—one far removed from the views experience by sensing light or heat (1994: 648). The intensity of radar returns is influenced by a variety of factors (Lillesand and
Kiefer, 1994: 669). The surface roughness of the geographic area affects the appearance
of the radar image (Jensen, 2000:310). Microwave radiation uniquely interacts with
vegetation, water, and urban features. Features have unique moisture contents that affect
the amount of radar energy reflected back to the sensor.
2.4.3 LiDAR Images
LiDAR (Light Detection and Ranging) uses a laser attached to an airplane to
detect the elevation and location of features in a particular geographic area. The laser
wavelength used to produce a LiDAR image is 0.90 µm (Jensen, 2000:285). LiDAR has
many applications including design of drainage systems and coastal structures, alignment
of highways, earthwork projects, modeling flood hazards, determining timely elevation
data, and post emergency management. LiDAR is often combined with spectral imagery,
such as black and white panchromatic; to provide a product that has horizontal and
vertical information (Gutelius: 1998:246-253). LiDAR technology offers an alternative
to traditional field surveying or photogrammetry methods of determining feature
elevations in two-dimensional imagery. “Photogrammetry is the art and science of
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making accurate measurements by means of aerial photography” (Jensen, 2000:137).
LiDAR requires less time and personnel training compared to these other techniques.
2.4.3.1 Characteristics
Earth features can be measured with an accuracy of 0.2 meters horizontally and
0.1 meter vertically. LiDAR imagery is a record of the location and elevation of these
features. This technique makes data easier to obtain without the need for extensive image
analysis (Priestnall et al, 2000:65-68). Photogrammetric image analysis requires multiple
images and many man-hours to interpret the data from the photographs. However, using
this method, many characteristics can be determined such as precise height, length, area,
perimeter, grayscale tone or color, and location of features, as well as photographic scale
(Jensen, 2000: 137). According to Jensen, LiDAR makes it easier to obtain these
characteristics because “each LiDAR measurement is individually georeferenced” (2000:
327). With LiDAR, characteristics of the aircraft, laser, atmosphere, and earth features of
interest are simultaneously recorded resulting in three-dimensional georeferenced
coordinates (Jensen, 2000: 327). After the remote sensing information is gathered,
processing of LiDAR data can be done easily with a computer. Miotto states, “One
advantage of using LiDAR to acquire elevation data is that it radically reduces processing
time” (2000:8).
LiDAR imagery can be collected day or night in any geographic region. This is a
benefit over surveying of rough terrain (Jensen, 2000: 326-327). Collection is also
possible with a minimum amount of cloud cover and precipitation over the area of
interest (Broshkevitch, 2002).
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2.4.3.2 Potential Uses
Analysts use LiDAR to develop landscape contours, spot heights, building
footprints, drainage patterns, watershed delineation, power line detection, and 3-D
rendering (Broshkevitch, 2002).
Computer generated landscape contours can be easily created with LiDAR data.
The user can designate the contour interval desired with the accuracy of the data as a
limit. Figure 2-30 is a contour image developed with LiDAR data. This digital elevation
model (DEM) shows the bare earth with trees, buildings, and other aboveground features
removed. The image has 1-meter contours.
Figure 2-30: LiDAR Contour Image (Broshkevitch, 2002)
Computer generated vegetation images and attributes can be easily created with
LiDAR data. Data such as average height, average stem spacing, percent canopy
coverage, and vegetation polygon area can be generated. Figure 2-31 is a digital
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elevation model (DEM) that shows trees and buildings. A DEM includes data on the
elevation and location of each feature in the image. Each tree has a node that links to a
database of information with tree height and location. A polygon is drawn,
encompassing the trees and allowing analysts to determine the area of canopy coverage.
Figure 2-31: LiDAR Vegetation Capture (Broshkevitch, 2002)
Figure 2-32 shows the attribute data that corresponds to Figure 2-31. The GIS
pop-up window shows the data contained in the database for the forest polygon. The
database includes information on average height, area of forest coverage, density, and
tree stem spacing.
Hydrology modeling is another application using LiDAR data. Figure 2-33 shows
the potential drainage, watershed analysis, and flood prediction of the geographic area.
The drainage patterns are indicated in dark blue and light blue. The watershed is
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Figure 2-32: LiDAR Attribute Data (Broshkevitch, 2002)
indicated with a polygon shaded green. Potential flooding is indicated in the gray shaded
area.
Figure 2-33: LiDAR Hydrology Modeling (Broshkevitch, 2002)
Analysts can easily produce flood hazard models using LiDAR data and computer
processing as shown in Figures 2-34 and 2-35. The LiDAR imagery contains elevation
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data for man-made features relative to the topography. LiDAR can be modeled in a
computer to show the structures at risk during a flood. Planners can then determine what
preventative actions to take or where to locate buildings in the future (Priestnall et al,
2000:65-70).
Figures 2-34 and 2-35 are three-dimensional computer simulations of geographic
areas that are a high flood risk. Figure 2-34 shows a town along a river without flooding.
Figure 2-35 is the same town after a flooding simulation (Priestnall et al, 2000:68). With
the aid of these models, planners can design scenarios for evacuation, and preventative-
zoning regulations can be implemented.
Figure 2-34: LiDAR Simulation (Priestnall et al., 2000:69)
Three-dimensional images are also possible with LiDAR to show a landscape
with buildings and vegetation. Figure 2-36 is useful to planners for determining how
changes in vegetation or buildings may affect the geographic area.
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Figure 2-35: LiDAR Simulation (Priestnall et al., 2000:69)
Figure 2-36: Three-Dimensional Image (Broshkevitch, 2002)
As with radar, LiDAR is less sensitive to atmospheric conditions than spectral
imagery, and can measure elevations in poor weather conditions. Determining elevations
with traditional photogrammetry requires two images of the same geographic area, time,
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and training. When Computer Aided Design (CAD) objects are superimposed onto a
model that has been developed with the help of LiDAR, the resulting three-dimensional
product can be helpful for analysis. Urban planning may be easier using a three-
dimensional view (Priestnall et al, 2000:65-68).
LiDAR is valued for its ability to collect accurate laser information in a timely
manner and its versatile applicability. When Hurricane Opal struck Florida in 1995,
LiDAR was used to map 200 km of shoreline in about two hours. This data was used to
produce a computer-generated contour map within two days (Gutelius: 1998:247-249).
Laser remote sensing was used in Moscow to map 5,000 km of transmission lines.
This process took only 10 days and achieved 15-20 cm accuracy, outperforming
traditional methods of stereo photogrammetry. Furthermore, because expensive fiber
optic cable was being installed, LiDAR precision resulted in a greater cost savings for the
project (Gutelius: 1998:249).
LiDAR provides 20 cm horizontal and 10 cm vertical accuracy. LiDAR data of
large geographic areas can be collected by aerial platform in a timely manner. The data
can be used to produce computer-generated models much faster than traditional
photogrammetric methods.
2.4.4 Digital Image Availability
Digital Images are available through many government and private sources.
Business agreements exist between government entities and private companies to
distribute the available images. Private companies often tailor their image products to
solve specific problems.
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The National Imagery and Mapping Agency (NIMA) should be considered first
when acquiring digital imagery. A DoD organization can acquire imagery through
NIMA or directly from a commercial company. NIMA has existing imagery license
agreements with commercial companies called Indefinite Delivery/Indefinite Quantity
(ID/IQ) Government Wide Agency Contracts (GWAC). With these contracts the
government can receive discounts for ordering a larger supply of imagery and licensing
arrangements. Any United States Government organization can receive imagery from the
commercial company by specifying the contract number, but the Government
Intelligence Community has priority. Commercial imagery from NIMA comes with
many rules on how it can be used. Each user must determine their own needs for the
imagery and operate according to the contract and NIMA guidelines (Commercial
Imagery Copyright & License Interim Guidance, 2002).
NIMA has imagery available from commercial companies, civil organizations,
and foreign commercial companies. Commercial companies include Aerial Images,
EarthWatch, ORBIMAGE, Space Imaging, and SPOT Image. Aerial Images provides
SPIN-2 imagery. EarthWatch has Quickbird-1, 2; STAR 3i by Intermap; and Russian 1-2
meter imagery. ORBIMAGE has OrbCities; OrbView-2, 3, 4; RADARSAT-1; and
SPOT-1, 2, 3, & 4. Space Imaging provides ERS-1, 2; IKONOS; JERS-1; LandSat-4, 5;
and RADARSAT-1. SPOT Image has ERS-1, 3; OrbView-2, 3, 4; RADARSAT-1; and
SPOT-1, 2, 3, & 4. A government civil organization such as USGS is a source of aerial
imagery (Commercial Imagery Copyright & License Interim Guidance, 2002).
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2.4.5 Digital Image Cost
NIMA provides commercial imagery to Department of Defense organizations at
minimal cost under DoD/Title 50 from their Commercial Satellite Imagery Library
(CSIL). In cases where a customer needs imagery not available in the library or needs a
broader distribution license for sharing with non-DoD organizations, the customer may
have to pay the additional costs. Commercial imagery includes any data that is available
on the commercial market including imagery from commercial companies here and
abroad, and organizations such as NASA and USGS (Commercial Imagery Copyright &
License Interim Guidance, 2002).
2.4.6 Digital Image Limitations
Digital images have limitations in the areas of weather, collection of the
appropriate features, high enough resolution for certain applications, satellite imagery
system resolution configuration, and cost of on-demand imagery. Each imagery system
and type of imagery has limits to the applicability of the imagery to specific
requirements.
One of the limitations of LiDAR is that it is weather sensitive. LiDAR sensors
are most effective under good weather conditions. Cloud cover will prevent sensors from
collecting data about earth features. Vegetation can also be a hindrance. Dense
vegetation will impede LiDAR pulses from reaching the ground, preventing analysts
from obtaining elevation models and distinguishing features such as buildings from trees
(Airborne LIDAR Topographic Surveying, 2002; Jensen, 2000: 329).
There are limitations of digital imagery when obtaining socioeconomic data from
an urban landscape. Many socioeconomic characteristics require less than 1-meter spatial
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resolution. Newer satellite imagery from IKONOS, Quickbird, and OrvView-3 meet this
spatial resolution requirement. However, socioeconomic research, such as a traffic and
parking study, requires five to sixty minute temporal resolution. The only imagery
source that meets this requirement is a high-cost aerial platform (airplane) or a camera
mounted from a high location such as a pole or building (Jensen, 2000: 466-467).
Multi-spectral imagery from sensors such as Landsat TM is configured for a
specific spatial, spectral, and temporal resolution. If alternative resolutions are required
to solve a given problem, then an aerial platform must be used instead of a satellite
platform (Jensen, 2000: 209). Obtaining this on-demand aerial imagery may be more
costly as discussed in Section 2.3.6.
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III. Methodology
3.0 Introduction
The methodology outlines the process of determining which type of remote
sensing imagery, when added to GeoBase, will provide enhanced mission capability. The
organization of the methodology is as follows: Set up of a GeoBase Laboratory,
Development of an Applications List, Analysis of the information, and Validation. The
researcher learned about GIS by setting up a student laboratory, acquiring the necessary
hardware to support GIS software, and completing an online course. The Applications
List is a compilation of research-backed uses of remote sensing imagery for solving
specific requirements. The list was narrowed to only those applications relevant to the
Air Force. The analysis process determines how remote sensing imagery can best be
applied to various Air Force requirements. Validation of this research and methodology
is accomplished by testing the imagery matrix with people at three sample Air Force
bases. Three steps are involved in applying the imagery matrix. First, determine the
specific imagery that will meet the user’s requirements. Second, acquire the appropriate
imagery and provide it to validation bases. Third, assess the usefulness of the matrix
through feedback from base users, as well as the researcher’s observations.
3.1 GeoBase and GIS Learning
GeoBase and Geographic Information System (GIS) concepts need to be
understood before imagery solutions can be explored. Several steps were followed to
meet this objective including setting up a GeoBase laboratory, progressing through a
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professionally recognized training program, and obtaining imagery samples for
familiarization.
3.1.1 GeoBase Laboratory
To gain a familiarization with the concept of GeoBase and how to operate a GIS,
a computer laboratory was needed. The laboratory must include compatible hardware
and software, and Internet capability. A suitable hardware configuration was determined
through discussions with GeoBase administrators throughout several Major Commands,
as well as obtaining information about the required hardware to operate GIS software and
save imagery data.
The computer hardware in the laboratory has the latest capability processing
speed and data storage. The computer has a 1.6Ghz processing speed that will enable the
timely processing of images. The 40 GB hard-drive provides enough storage space to
save data necessary for research purposes. Images require a large amount of data storage
space. The computer also has 264 KB of random access memory (RAM). This is
essential to the processing speed of the computer. Images also require a large amount of
RAM for timely processing. The time it takes to open and manipulate an image depends
on the computer’s speed and memory. If a user runs multiple applications
simultaneously, the computer’s processing will be slowed.
Environmental Systems Research Institute, Inc. (ESRI) ArcGIS 8.1 software was
used to analyze the imagery in a GIS database. This is a common software program used
by many civilian and government organizations. The software is recognized as one of the
leading GIS packages available, and has been adopted by many Air Force Bases.
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An Internet connection capability was required to access the latest information on
GeoBase and GIS, download available imagery, and complete training through an online
tutorial. GeoBase program information is accessible through the official GeoBase
website. Numerous websites provide an explanation of GIS concepts. Downloading
imagery through the Internet is one way to collect data for the GeoBase. Imagery from
government agencies and commercial companies can be sent through the mail on a
compact disc. The Internet was essential to GIS learning through a website,
www.esri.com, determined as the best training alternative for this research.
3.1.2 GIS Training
GIS training may be accomplished in several ways including ESRI online
courses; alternative GIS online courses; GIS coursework through local universities; and
GIS short courses offered by the Army’s Defense Mapping School in Fort Belvoir, VA.
ESRI produces the GIS software needed to build a GeoBase, and provides many free
courses to Department of Defense employees through the National Imagery and Mapping
Agency (NIMA). The online courses are self-paced, and students have up to one year to
complete each course upon registration. Self-paced courses allow the student to easily
coordinate around other coursework or employment requirements. With an Internet
connection and the appropriate software and hardware, the student can learn from any
location. The researcher chose this option to further his knowledge about GIS.
The researcher opted to take the GIS course offered by ESRI, despite the
availability of other GIS courses, because ESRI was also the designer of the software.
This is the same software applied to GeoBase and used by many Air Force installations.
ESRI provided data formats such as computer aided drawing (CAD), database tables, and
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imagery to learn how to manipulate and use the GIS software. Practice sets of data,
exercises, and exams reinforced the material. This training provides a foundation for
understanding how imagery is used in a GeoBase program.
Local college classes would require a specific schedule that did not fit well with
the timing of this research. Also, the software used at Wright State University was not
the latest version, ESRI ArcView 8.1. The University of Cincinnati and Ohio State
University would be good options for quality instruction, but they are over 60 miles from
the Air Force Institute of Technology. In 1998, the researcher completed a six-week GIS
course at the Defense Mapping School, Fort Belvoir, Virginia. The distance and time
required to take follow-up courses in Virginia was not practical.
3.2 Air Force Base Applications List
Identifying the Air Force mission applications that will benefit from this research
was necessary to develop a useful matrix. This applications list focuses this research on
Air Force questions and challenges unique to Air Force operations. Several options for
identifying these missions were considered.
3.2.1 Options to Determine Air Force Applications List
An Applications List could have been generated by several sources, including Air
Force Mission Statements, existing research through literature review, coordinating with
MAJCOM Geo-Integration Officers, and surveys. Mission statements are available for
Air Force organizations at all levels from MAJCOMs to the squadrons. Developing a
applications requirements list through mission statements would be a time-consuming
and inefficient approach, as the researcher would have to gather and breakdown very
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broad mission statements into specific activities. This option does not consider creative
inputs from personnel in the field. Moreover, Air Force mission statements may not
reflect the evolving nature of the GeoBase program.
Literature review provided many imagery applications that could be applied to
Air Force problems. These were incorporated into the Applications List. Current
literature identified proven civil engineering imagery applications. MAJCOM GIO
Officers, who represent a limited group of respondents, could have provided some
application inputs based on broad mission requirements. The AF/GIO survey used by the
researcher reflects proven as well as potential civil engineering applications. By
inquiring at the squadron level, a larger survey group, the researcher gets a more
thorough understanding of how people are applying GeoBase in day-to-day operations.
Squadron-level personnel who are using GeoBase hands on can provide creative
feedback. A survey was selected as the best option because it was the most extensive,
encompassing many different squadrons and hundreds of respondents throughout the Air
Force.
3.2.2 Air Force Applications List Decision
Science Applications International Corporation (SAIC) conducted a GeoBase
survey for the AF/GIO in April 2002. The survey included a section on missions that
presently use GeoBase. Survey respondents also indicated potential uses of GeoBase in
their jobs to accomplish many varied missions. Information from the survey and the
literature review was combined to make a single applications list. This list was used to
determine the imagery that is best suited for each application. In most cases, multiple
types of imagery are useful to mission accomplishment.
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3.3 Analysis
The researcher will analyze how each Air Force application can be aided with the
use of imagery. Because multiple sources and types of imagery exist, this research
matrix will provide a systematic way to determine the appropriate imagery for each
application.
3.3.1 Potential Analysis Methods
Several analysis methods could be used including value-focused thinking,
developing a list of all possible applications for a particular imagery type, applying
findings from literature, and matching each mission to the appropriate imagery.
In value-focused thinking (VFT), the researcher works with an expert decision
maker to determine what factors are important in solving a given problem. These factors
must be mutually exclusive and collectively exhaustive. Once these factors are
identified, the expert decision-maker assigns a subjective weight (importance) to each
factor. For instance, when analyzing imagery to aid missions, the decision maker may
identify spatial, spectral, and temporal resolution as factors that are important to choosing
the best alternative. The researcher and decision maker then identify possible solution
alternatives such as Multi-spectral, LiDAR or Radar imagery. Each alternative is scored
against the model hierarchy that was developed. The alternative with the highest score is
the best alternative.
The process of identifying the best alternatives can be accomplished with many
software applications including FORTRAN or Microsoft Visual Basic. Each of these can
be used to develop a software program specifically for determining the appropriate
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imagery for each Air Force application. FORTRAN is an older straightforward program,
while Visual Basic provides more complex capabilities.
The costs involved for each alternative are considered separately after all the
alternatives have been scored. In this thesis, the researcher decided not to use VFT for
several reasons. VFT relies heavily on an expert decision-maker to provide subjective
weights to the various factors important to the researcher. However, objective remote
sensing research is already available that will provide solutions for meeting mission
requirements. Often, VFT is used when solution alternatives are not readily known or
need to be determined. With imagery, all the possible alternatives are known. The
researcher only needs to determine which imagery is best for each mission.
Developing a list of all possible applications for a particular imagery type has
unlimited possibilities. This method of analysis would incorporate all available research
on imagery applications. Identifying all possible applications for each imagery type
would require an unreasonable amount of time. This method was eliminated because it is
not specifically relevant to solving Air Force mission problems.
Applying findings from literature review is one way to determine uses for
imagery. This method covers many different fields of discipline and applications. Some
of the applications gained through literature review are relevant and useful to Air Force
problems. These applications are incorporated into the matrix developed in this research.
Matching each mission to the appropriate imagery would provide the most
focused and objective way to determine the best use of imagery to solve Air Force
problems. This method would include AF/GIO survey information on mission
requirements and additional applications from literature review.
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3.3.2 Analysis Method Outlined
Matching each application to the appropriate imagery is selected as the method of
analysis. Researchers Jensen and Cowen developed Table 3-1 that presents Remote
Sensing of Urban/Suburban Infrastructure and Socio-Economic Attributes (1999). The
thesis matrix uses this table as a foundation for analysis. The table identifies
urban/suburban attributes and the minimum resolution requirements for each.
The first column in the table is labeled attributes. The attributes are common
requirements of organizations such as government planning agencies, the Department of
Transportation, utility companies, public service commissions, and the Department of
Emergency Management (Jensen and Cowen, 1999:611). For instance, the Department
of Transportation often conducts traffic count studies to determine if additional traffic
lights are required. Panchromatic or visible aerial imagery acquired every 5-10 minutes,
at 0.25-0.5 meter spatial resolution would help planners determine information about a
particular intersection. The remaining three columns indicate the minimum resolution
requirements for each attribute.
The temporal resolution in Table 3-1 is managerial temporal resolution. The
managerial temporal resolution is how frequently managers need new imagery data to
fulfill the attribute requirement. For instance, under the category Utility Infrastructure,
U1-general utility line mapping and routing would be completed every 1-5 years so
managers have current data for these projects.
Jensen and Cowen identify two additional types of temporal resolution, not shown
in Table 3-1: urban development cycle and imagery system revisit. The urban
development cycle is the evolution of an urban area as the land-use changes over time.
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Jensen and Cowen give an example of a housing area that evolved from rangeland to
fully landscaped residential housing within one year (1999:611-612). The responsibility
to take urban development cycle into account rests with the analyst. Each geographic
area has a unique development cycle that must be taken into account during imagery
analysis. The development cycle will not be included in the research matrix due to
variability of geographic areas. Each analyst must know what development changes have
taken place that may not be accurately shown on archived imagery.
The imagery system temporal resolution is how often imagery can be collected of
the same geographic area by a satellite or aerial platform (Jensen and Cowen, 1999:614).
Aerial collection can be done on demand, weather permitting, providing imagery updates
every few minutes as an aircraft passes over the geographic area. Some satellites such as
IKONOS and Quickbird provide temporal resolution times of a few days, while others
such as Landsat revisit every 16 days. The temporal resolution is crucial to many time
sensitive applications such as disaster emergency response. The researcher will identify
the remote sensing temporal resolution for each satellite and aerial platform in the
research matrix.
Minimum spatial resolution required for each attribute is indicated in Table 3-2.
The spatial resolution shown here is the minimum spatial resolution required for the
analyst to complete the requirements of the corresponding attribute. For instance, the
minimum spatial resolution required for U1-general utility line mapping and routing is
1-30 meters.
Spectral resolution acronyms used in Table 3-2 are shown in Table 3-3. Jensen
and Cowen point out that most image analysts would agree that high spatial resolution
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(less than 5-meter) is more important that high spectral resolution (multi-spectral
capability). Any spectral band will work as long as there is sufficient difference between
the feature and the background. There are ideal sections of the electromagnetic spectrum
when determining certain information. The ideal spectral wavelengths are indicated for
each attribute in Table 3-1 (1999:612-613). For instance, U1-general utility line mapping
and routing is ideally accomplished with panchromatic, visible color, or near-infrared
imagery.
The main categories have all been highlighted blue in Table 3-1. The category
Land-Use/Land-Cover is a United States Geological Survey (USGS) classification
system for remote sensing imagery. Land-use indicates the activity for which the land is
being used, such as a state park. Land-cover indicates the features on the land such as
trees (Jensen and Cowen, 1999:614). The USGS classification system was developed to
provide land-cover information using remote sensing data. The classification system has
also been used for over 20 years to determine land-use information (Jensen, 2000:414-
417). An example of a Level I USGS classification is Urban or Built-up Land. Referring
to Table 3-1, L1-USGS Level I, an analyst requires (5-10 year) temporal, (20-100 m)
spatial, and (V, NIR, MIR, or Radar) spectral resolution minimums to determine the land-
use and land-cover for Urban or Built-up Land. USGS Levels II through IV have higher
resolution requirements as shown in Table 3-1.
Table 3-2 defines acronyms used in Jensen and Cowen’s table and diagram. This
spectral legend is later expanded to include acronyms used by the researcher in the
imagery decision matrix and imagery system key.
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Table 3-1: Urban/Suburban Attributes and Resolution (Jensen and Cowen, 1999:612).
Minimum Resolution Requirements Attributes Temporal Spatial Spectral Land-use/Land-Cover L1-USGS Level I 5-10 years 20-100 m V-NIR-MIR-RadarL2-USGS Level II 5-10 years 5-20 m V-NIR-MIR-RadarL3-USGS Level III 3-5 years 1-5 m Pan-V-NIR-MIR L4-USGS Level IV 1-3 years 0.25-1 m Pan Building and Property Infrastructure
1-5 years 0.25-0.5 m Pan-V B1-building perimeter, area, height and cadastral information (property lines) Transportation Infrastructure T1-general road centerline 1-5 years 1-30 m Pan-V-NIR T2-precise road width 1-2 years 0.25-0.5 m Pan-V T3-traffic count studies (cars, airplanes, etc.) 5-10 minutes 0.25-0.5 m Pan-V T4-parking studies 10-60 minutes 0.25-0.5 m Pan-V Utility Infrastructure U1-general utility line mapping and routing 1-5 years 1-30 m Pan-V-NIR U2-precise utility line width, right-of-way 1-2 years 0.25-0.6 m Pan-V U3-location of poles, manholes, substations 1-2 years 0.25-0.6 m Pan Digital Elevation Model (DEM) Creation D1-large scale DEM 5-10 years 0.25-0.5 m Pan-V D2-large scale slope map 5-10 years 0.25-0.5 m Pan-V Socioeconomic Characteristics S1-local population estimation 5-7 years 0.25-5 m Pan-V-NIR S2-regional/national population estimation 5-15 years 5-20 m Pan-V-NIR S3-qualtiy of life indicators 5-10 years 0.25-30 m Pan-V-NIR Energy Demand and Conservation E1-energy demand and production potential 1-5 years 0.25-1 m Pan-V-NIR E2-building insulation surveys 1-5 years 1-5 m TIR Meteorological Data M1-weather prediction 3-25 minutes 1-8 km V-NIR-TIR M2-current temperature 3-25 minutes 1-8 km TIR M3-clean air and precipitation mode 6-10 minutes 1 km WSR-88D Radar M4-severe weather mode 5 minutes 1 km WSR-88D Radar M5-monitoring urban heat island effect 12-24 hours 5-30 m TIR Critical Environmental Area Assessment C1-stable sensitive environments 1-2 years 1-10 m V-NIR-MIR C2-dynamic sensitive environments 1-6 months 0.25-2 m V-NIR-MIR-TIR Disaster Emergency Response DE1-pre-emergency imagery 1-5 years 1-5 m Pan-V-NIR DE2-post-emergency imagery 12 hours-2 days 0.25-2 m Pan-V-NIR-RadarDE3-damaged housing stock 1-2 days 0.25-1 m Pan-V-NIR DE4-damaged transportation 1-2 days 0.25-1 m Pan-V-NIR DE5-damaged utilities, services 1-2 days 0.25-1 m Pan-V-NIR
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Table 3-2: Spectral Legend
SPECTRAL LEGEND: Pan=Black and white panchromatic (0.5-0.7µm) V=visible color (0.4-0.7µm) NIR=Near-infrared including black and white infrared, color infrared (0.7-1.1µm) MIR=Mid-infrared including black and white infrared, color infrared (1.5-2.5µm) TIR=Thermal infrared (3-12µm) M=Multi-spectral (Includes V, NIR, MIR, and depending on sensor; TIR) L=Light Detection and Ranging (LiDAR) Note: LiDAR is used mainly for elevation data; an additional imagery system such as Pan is used to meet the requirements of the mission WSR-88D Radar=ground-based National Weather Service Weather Surveillance Radar Radar=Aerial Radar
USGS Level I through Level IV Categories are used in Jensen and Cowen’s table.
Table 3-3 is an example of the USGS Land-Use/Land-Cover Classification System
(Jensen, 2000:416).
Table 3-3 USGS Level I-V (Jensen, 2000:416).
Classification Level Level I 1 Urban or Built-up Land Level II 14 Transportation, Communications, and Utilities Level III 141 Transportation Level IV 1411 Roads and Highways Level V 14111 Dirt Level V 14112 Paved Level V 14113 Limited access (freeway, toll) Level V 14114 Interchange Level V 14115 Parking Level V 14116 Bridge
In addition, a Jensen and Cowen developed a figure that shows the imagery that
can be used for each attribute. The thesis researcher will transfer the imagery
information from the figure to the urban/suburban attributes table. Next, the researcher
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will add three more columns to the table: Air Force Mission, Imagery Availability, and
Cost.
The Applications List is finalized using the AF/GIO survey and literature review
imagery applications. The researcher will identify applications that can logically be
consolidated. In the case of missions that cannot be aided by current imagery
capabilities, the researcher will list these at the bottom of the table.
The researcher will match the Air Force application to Jensen and Cowen’s
attributes table. If there is no match, then the mission will be input into the USGS
generalized section of the table. Each mission will match to only one attribute. The
attributes will have corresponding minimum resolution requirements, imagery type,
availability, and cost.
3.4 Validation
Validation is the process of determining the usefulness of the thesis research and
whether it can be applied in operational scenarios. Several methods can be used to
validate this research, which are described in the next section.
3.4.1 Potential Methods of Validation
The researcher considered four validation methods: field test, MAJCOM/GIO
survey, imagery expert survey, and researcher-controlled limited field test. The field-
testing method would implement the matrix at various bases and allow users to determine
the usefulness of the matrix over time. Because the researcher did not test the matrix in
advance, this method involves higher risk. Base personnel, in trying to the implement the
unproven matrix, may waste time and money in the form of personnel hours and costly
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imagery. MAJCOM/GIO and imagery expert surveys do not provide the broad and
varied feedback that would be required for thorough validation. There are remote sensing
experts on the thesis committee that provide initial validation. The researcher-controlled
limited field test would afford immediate feedback using three Air Force bases. This
method is designed to provide a broad range of missions and keep personnel time and
cost to a minimum.
3.4.2. Validation Method Chosen- -Researcher Controlled Limited Field Test
The researcher will use civil engineering missions from three active duty Air
Force bases. In this scenario, base personnel are more involved in testing the matrix and
can provide the researcher with valuable feedback. After the three designated bases have
validated the matrix, other Air Force bases may trust it more as a useful tool.
This method can be applied quickly with minimal impact on money and man-hours, and
results are prompt. GeoBase is in the implementation phase; therefore an imagery
decision tool is needed immediately. Moreover, this method will tackle specific
problems without delay.
Three validation bases were chosen based on the following criteria: diversity of
missions, in-house GeoBase and GIS expertise, level of GeoBase implementation, and
potential for future research coordination. The validation involves two bases from
different major commands (MAJCOMs) and one Direct Reporting Unit (DRU): Wright-
Patterson Air Force Base, Air Force Material Command (AFMC); Elmendorf Air Force
Base, Pacific Air Forces (PACAF); and The United States Air Force Academy (AFA).
Wright-Patterson Air Force Base (WPAFB) in Dayton, Ohio has a GeoBase
Program that is in an early implementation stage. Because WPAFB is still in the planning
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phases, this research will help determine appropriate imagery for mission
accomplishment. Wright-Patterson AFB conducts laboratory research and is the Air
Force leader in acquisition and development. Aeronautical Systems Center (ASC) is the
largest unit at Wright-Patterson Air Force Base with a work force of about 9,000 civilians
and military, and an annual budget of nearly $13 billion. Wright-Patterson AFB
encompasses 8,357-acres with two runways supporting about 48,000 aircraft operations
per year (United States Air Force Fact Sheet: Wright-Patterson Air Force Base, 2002).
PACAF is a leader in implementing the GeoBase program. By choosing a base
that already has an operational GeoBase, insight can be gained regarding the usefulness
of the research matrix. Elmendorf Air Force Base in Anchorage, Alaska is home of the
3rd Wing, which has 6,700 personnel (Third Wing Mission, 2002).
The United States Air Force Academy in Colorado Springs, Colorado covers
18,500 acres with elevations of up to 7,163 feet. Located at the Academy are special
classrooms, labs, and athletic fields for cadet training, and other facilities commonly
found on Air Force bases (Air Force Academy Public Affairs Fact Sheet, 2002).
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IV. Analysis and Validation
4.0 Introduction
The analysis and validation outlines the actions taken to determine which type of
remote sensing imagery, when added to GeoBase, will provide enhanced mission
capability. The organization of the analysis and validation follows the same guidelines as
outlined in Chapter 3: Set up of a GeoBase Laboratory, GIS Training, development of an
Air Force Base Applications List, Analysis, and Validation. Each of these will be
discussed in greater detail in the subsequent sections.
4.1 GeoBase and GIS Learning
The idea behind creating a GeoBase laboratory was for specialized training to be
conducted on GeoBase and GIS in a focused environment. Two students were working
on GeoBase issues and needed a laboratory with necessary hardware and software.
Because the field of remote sensing is relatively new to the AFIT schoolhouse, no formal
coursework or laboratories existed in-house to support this type of research.
4.1.1 GeoBase Laboratory
The GeoBase Laboratory consists of two high-end computers with appropriate
hardware and software capability to support GeoBase research. Computer system
requirements were determined based on feedback from Air Force MAJCOM and base-
level Geo-Integration Officers about the systems they use to operate their GeoBase. Each
computer has a 1.6 GHz processor, 264 Mb RAM, and 40 GB hard-drive. ESRI
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ArcView 8.1 software is used for the GeoBase. These computers provided appropriate
capability.
ENVI software by Research Systems or Image Analyst software by ESRI should
be added to the laboratory computers in the future to take advantage of the data contained
in multi-spectral imagery (Frohn, 2002). With ENVI, data is stored on a CD in different
spectral bands such as red, green, blue, and near infrared. The imagery spectral bands are
manipulated to determine much more detail than is possible with ESRI ArcView 8.1. An
image from Image Analyst can provide this analysis as well.
4.1.2 GIS Training
GIS training was obtained through the Environmental Systems Research Institute,
Inc. Taking an ESRI ArcView 8.1 introductory course was very helpful in developing
GIS knowledge. The self-paced course is taught online at ESRI’s website titled ESRI
Virtual Campus and can be accessed at http://campus.esri.com/. ESRI provides
temporary downloads of ArcView software to use for the course, but ArcView 8.1 was
already available to the researcher through AFIT. ESRI courses are available for free
from the National Imagery and Mapping Agency (NIMA), with one year allowed to
complete the course.
The course was organized into six learning modules: Basics of ArcGIS,
Displaying and Geo-referencing Data in ArcGIS, Working with Spatial Data in ArcGIS,
Working with Attributes in Arc GIS, Querying your Database in ArcGIS, and Presenting
Data in ArcGIS. Each of the modules developed the user’s capability to understand how
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a GIS is used, reviewed terminology associated with a GIS and ESRI ArcView 8.1
software, and taught how to manipulate a GIS to obtain useful information.
Each of these modules was completed using the ArcView 8.1 software. It was
helpful to print out the steps for each module and then work through them because
several windows are often open at the same time. ESRI’s estimated time for the
completion of a module was fairly accurate, taking about 2-4 hours for each, including
practice and final tests.
Practice tests reinforced the material, and a final examination was given at the end
of each module. The exams were straightforward and tested the ability of the student to
manipulate the software program to obtain answers using practice data sets. A score is
calculated that determines whether the student can advance to the next module. A 70
percent score is required to proceed to successive modules. Students can review their
answers and the tutorial and retake the exam if necessary. A benefit to the online course
is that it is self-paced, which gives the student flexibility.
In addition to reviewing GIS fundamentals, the researcher needed to acquire
imagery to further develop an understanding of current imagery of an Air Force base.
One of the Air Force bases used for validation had imagery readily available. The
purpose was to test the software capability and aid the researcher in understanding
strengths and limitations of the imagery. IKONOS imagery of the United States Air
Force Academy (USAFA) demonstrated the impressive capability possible at 1-meter
resolution (Portillo, 2002).
The researcher’s efforts to understand imagery and the capabilities of ESRI
ArcView 8.1 had a positive result. The Internet tutorial provided a foundation for
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understanding how data is used and manipulated in a GIS. By understanding how a GIS
works, and practicing with actual current imagery from an Air Force base, the researcher
developed an adequate familiarization that was valuable to the thesis effort.
4.2 Air Force Base Applications List
Developing the Applications List involved assimilating information from three
major sources including research from literature review, AF/GIO survey results from
April 2002, and insights from a 2002 Geospatial Technologies Symposium & Exposition.
Some examples of imagery applications identified in the literature review and from the
AF/GIO survey are shown in Table 4-1. There were many applications identified in the
literature review that addressed many of the application concerns in the AF/GIO survey.
The AF/GIO survey did not detail how imagery is used in GeoBase, but the requirements
of GeoBase imagery logically matched the applications identified in the literature review
in most cases. Seven additional applications were identified in the AF/GIO survey and
are discussed in more detail later in this section. More than 50 applications were
identified that are relevant to Air Force missions. These applications range from urban
infrastructure to natural resources management. The complete list of applications is
available in Column 1 of Appendix B, Imagery Decision Matrix.
The AF/GIO survey of Air Force GeoBase applications was reviewed for
appropriate Air Force mission applications. Eighty-five main operating bases responded
to the survey. There were more than 100 responses regarding GeoBase applications from
individual squadrons. The survey was formatted for data entry in several areas including
Air Force base survey identification number, characteristics about the application,
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contractors involved in implementing the application, and compliance with spatial data
standards. The application description was used for applications included in the
Table 4-1: Air Force Applications List Examples
Distance and area measurements, building footprintsFacility elevationsCommunity planningPopulation estimationUrban sprawlTransportation management (repairs)Traffic studies (parking; conjested areas)Digging Permits (site information)Highway alignment planningForest management(deciduous, evergreen, or mixed identification)Clear-cut forest identificationEnvironmental hazards IdentificationFire Response GPS/GIS Tracking SystemFire Management
Applications List. Applications of GeoBase ranged from infrastructure measurements
and management to wartime training and tasks.
Table 4-2 contains some examples of applications provided by the survey. The
responses shown indicate an open-format style format. Each of the applications was
reviewed to determine if imagery could enhance the user’s ability to accomplish the
requirement. For instance, the first application shown in Table 4-2 is urban forest
database. This application is accounted for in the Forest management section of the
Applications List.
The second application listed in the survey indicates water, sewer, storm water
sewer coverage loader. Additional information is provided for some of the applications
such as, set edits environment, which is not important to determining imagery
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applications for GeoBase. This application is incorporated under Facility management
and Flood prevention and planning tool in the Applications List.
Some survey applications had no direct correlation to the use of imagery. The
next application listed, ArcView map production extension, is an example of an
application that was not incorporated into the Applications List. This application is
simply a computer application that is not relevant to determining an Applications List.
Many of the applications identified on the AF/GIO survey from April 2002 were already
accounted for through literature review. Several more had no relevance to the
Applications List developed. Several new applications were identified from the
information provided in the survey. A discussion of these follows.
Table 4-2: GeoBase Application Examples (AF/GIO survey, 2002).
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urban forest databaseWater, sewer, stormwater sewer coverage loader, set edits environmentArcView map production extensionArc/Access interface to query airfield obstructionsenvironmental restoration applicationNEPA environmental assessment applicationA file manager with tool to retrieve and group themes. it also connects themes to database and legend files.Tool that edits shapefiles.Geospatial Event Response System - Used basewide for planning and initiating responses to emergency events.Customized application, used to provide basewide access to CIP.Application built with purpose of integrating data sources such as other mission databases outside the CIP.Application built with the purpose of accessing quick and easy maps of the installation, with some query ability.Fire Department application which integrates the CIP with the ACES-FD module. Ties event information to the map.Application used to identify UXO hazard areas and monitor clearance actions. Will have access from the Web in the future.Used to manage mobile and static target status, condition and location. Will have access from the Web in the future.Used to track profile utilization rate and thus better manage the siting of future range assets. Will have access from the Web in the future.Used to plan and track the maintenance of Reservation roads and gates in support of missions. Will have access from the Web in the future.Smoke modeling for Wildland & Urban Interface Fire Management
Seven new applications were identified from the survey. The seven applications
include aircraft accident investigations, asbestos identification, noise contour mapping,
Rapid Runway and Repair (RRR)/Minimum Operating Strip (MOS) operations,
Unexploded Ordinance (UXO) identification, mapping of airborne chemical plumes, and
target asset management.
Aircraft accident investigation is possible with imagery available commercially
and was added to the Air Force Applications List. Asbestos identification and noise
contour mapping require further imagery research to determine if these are possible.
These are included in the Applications List with the note (Future application). With
satellite advances, these two imagery applications may be possible in the future.
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Rapid Runway and Repair (RRR)/Minimum Operating Strip (MOS) operations,
Unexploded Ordinance (UXO) identification, mapping of airborne chemical plumes, and
target asset management were not included in the Applications List because these are
assumed to be wartime requirements. Additionally, airborne chemical plumes can not be
detected with the imagery researched, but may be possible with hyper-spectral imagery
discussed later in Section 5.4, Future Research. Wartime requirements will have
classified imagery to exploit with more capability than imagery systems researched for
this thesis. Additionally, many of the recommendations for collecting aerial imagery will
be unique because of the high-risk to aircraft. Aircraft may be at risk of attack when
flying within the range of surface to air missiles and anti-aircraft artillery in a war zone.
For this reason, war zone remote sensing aerial platforms need to operate at a much
higher altitude than for peacetime collection. The Air Force GeoReach program should
incorporate these applications into a decision tool for wartime purposes.
The survey findings presented several challenges and limitations. Many
responses provided minimal explanation, and the survey results concerning applications
came from only 35 of the 85 participating bases. The data was limited in that only Air
Force Civil Engineer (CE) personnel completed the survey, rather than several squadrons
on each base. According to AF/GIO, future GeoBase surveys would include many more
participating squadrons. Despite the limitations, the survey still provides insight into
GeoBase applications.
The 2002 Geospatial Technologies Symposium & Exposition confirmed the
applications found in the literature review and their relevance to Air Force requirements.
Applications presented on various military and civilian projects provided further
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understanding of GIS and the appropriate uses of imagery. One application presented on
LiDAR significantly helped in understanding the products available with LiDAR data.
LiDAR is discussed in more detail in Chapter 2.
A comprehensive Air Force Applications List was developed from the literature
review, AF/GIO survey, and the 2002 Geospatial Technologies Symposium & Exposition
The list includes more than 50 imagery applications that can benefit GeoBase, and these
are shown in the Imagery Decision Matrix, Appendix B.
4.3 Analysis
The analysis method is explained and then five sub-sections of the analysis are
discussed including Jensen and Cowen’s table and diagram used as a foundation for the
decision matrix, an initial and final version of the imagery decision matrix, and an initial
and final version of the Imagery System Key. Jensen and Cowen developed a table and
diagram for identifying the minimum imagery required to meet particular urban and
suburban requirements shown in Section 3.3.2. Their work was used as a starting point
for developing a decision tool to determine particular imagery types best suited for
specific Air Force applications. An initial imagery decision matrix and imagery system
key was developed from the literature review and Jensen and Cowen’s table and diagram.
Later, significant revisions to the matrix and key were made which streamlined the
decision process and made the tool more user-friendly. Each of these sections will be
discussed in more detail to follow.
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4.3.1 Analysis Method
Microsoft Excel spreadsheet software was used to create an imagery decision
matrix. Before building the spreadsheet, urban and suburban applications of remote
sensing imagery were reviewed (Jensen and Cowen, 1999). Jensen and Cowen’s
information contained in a table and diagram, presented imagery applications for
managers to use in decisions concerning infrastructure, environmental management,
socioeconomic characteristics, and emergency response. Jensen and Cowen’s table is
shown in Table 3-1. Jensen and Cowen’s diagram, shown in Figure 4-1, is used with the
table to determine the current remote sensing systems that can fulfill the attribute
requirements outlined in the table. Information was combined from both the table and
Figure 4-1 into one spreadsheet, the imagery decision matrix. This reduced the level of
complexity posed by the original table and Figure 4-1, and was the first in a series of
steps to develop an imagery decision matrix for Air Force GeoBase.
4.3.2 Jensen and Cowen Table and Diagram Foundation
There are several reasons to use Jensen and Cowen’s research as a starting point
for developing the imagery decision matrix. The table and diagram present a significant
amount detail that is useful to this research. Furthermore, the urban and suburban
attributes represented in Jensen and Cowen’s table have many similarities to Air Force
base applications. Even with the foundation provided by Jensen and Cowen’s table and
diagram, many formatting and content improvements were made to tailor the information
into an Air Force imagery decision matrix. The discussion that follows provides further
details about Jensen and Cowen’s diagram.
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Jensen and Cowen’s diagram that is used with Table 3-1 is shown in Figure 4-1.
The x-axis represents nominal spatial resolution (meters), and the y-axis represents
temporal resolution (minutes). To use the diagram, first identify an urban/suburban
attribute code listed in Table 3-1, such as C2-dynamic sensitive environments. C2-
dynamic sensitive environments require a minimum temporal resolution range of 1-6
months and a minimum spatial resolution range of 0.25 to 2 meters. A manager can
obtain imagery with one of the minimum spectral resolutions required noted as visible,
near infrared, mid infrared, or thermal infrared to fulfill requirements of the dynamic
sensitive environments attribute.
The C2 ellipse is located in the upper left portion of Figure 4-1. Many shaded
rectangles are included on the diagram. Each of these represents an imagery system such
as aerial imagery, Quickbird, IKONOS, and others. Jensen and Cowen use arrows to
point to a rectangle with an adjacent list of imagery systems. The rectangles fill in the
area of the graph for the spatial and temporal resolution of each imagery system. The area
of the graph (Figure 4-1) covered by a rectangle represents the spatial (x-axis) and
temporal (y-axis) resolution capability of each imagery system. Attributes presented in
Jensen and Cowen’s table (ellipses) are overlaid onto imagery systems (rectangles) that
represent temporal and spatial resolution. The minimum spatial and temporal resolution
requirements of C2-dynamic sensitive environments are fulfilled by aerial imagery
because it is superimposed over top of the rectangle representing aerial imagery. A user
of this diagram must also envision that the ellipses may be shifted side to side and up and
down, overlapping different rectangles. Hence, more than one imagery system will
provide for the minimum resolution requirements of C2-dynamic sensitive environments.
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Rectangles that have either lower (more precise) spatial resolution and/or lower (less time
required) temporal resolution will also fulfill the requirements. In the case of C2,
Quickbird and Space Imaging IKONOS are applicable. This is demonstrated by shifting
the C-2 ellipse downward. The imagery systems Quickbird and IKONOS are intersected
by the C-2 ellipse, therefore they also will fulfill the requirements of C2.
Because the diagram is visually complex, both the table and diagram were
combined into one simplified imagery decision matrix. This allows a manager to
determine the appropriate imagery for a particular application all in a spreadsheet format.
Jensen and Cowen’s table formed an initial framework for the matrix, and an
additional column was added for imagery systems represented in the diagram, as well as
those researched through literature review. A discussion of the construction of the
imagery decision matrix follows.
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Figure 4-1: Diagram of Resolutions (Jensen and Cowen, 1999:613).
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4.3.3 Imagery Decision Matrix - Initial
The Imagery Decision Matrix is used to determine what type(s) of remote sensing
imagery are applicable for a specific Air Force requirement. Many different formats are
possible to display information contained in the imagery decision matrix. There are
multiple ways to combine applications, imagery, and resolutions. The format presented
here represents an optimum way to view and use the information. In order to minimize
the difficulty in using the imagery decision matrix, an imagery system key and instruction
sheet were developed. These are discussed in later sections. The final version of the
imagery decision matrix and imagery system key is presented in later sections, however,
demonstrating the initial formats shows how this tool evolved. The initial imagery
decision matrix is shown in Appendix A. The matrix can be used to determine the
imagery that best suits a specific task or as an educational tool to learn about the imagery
types and capabilities that are available. The imagery decision matrix consists of seven
columns, which are discussed in greater detail.
Column 1 includes Air Force applications that were identified according to
Section 4.2 and were matched to the attributes in Column 2 from the Jensen and Cowen
table. Some applications and attributes were easy to match such as traffic studies and T3-
traffic count studies (cars, airplanes, etc.). Other applications were not as easily matched
to an attribute. The distance and area measurements application was matched to the B-1-
building perimeter, area, height and cadastral information (property lines) attribute. This
was a logical position for the distance and area measurements application, although it
could have been placed under more than one attribute or a different attribute such as U2-
precise utility line width, right-of-way. Table 4-3 shows examples of the Air Force
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applications and attributes contained in the imagery decision matrix that were matched to
the B-1 attribute.
Column 2 is the attributes column from Jensen and Cowen’s table. The attributes
are identified according to sub-headings such as Land-Use/Land-Cover and Building and
Property Infrastructure. Under each sub-heading, there may be one or more categories.
For example, under the Land-Use/Land-Cover subheading, there are four categories: L1
through L4 corresponding to USGS Level I-IV. The United States Geological Survey
(USGS) Levels I-IV are classifications, first devised in the 1970s, that indicate the
capability of a remote sensing image. The USGS classification system matches specific
land-use and land-cover with remote sensor data characteristics. Table 4-3 is an example
of information in the attributes column for B1-building perimeter, area, height and
cadastral information.
Table 4-3: Applications Table 1.
1. Applications 2. Attributes
Digging PermitsCommunity planningBuilding footprintsSpot heightsFacility elevations3-dimensional renderingFacility management
Imagery Decision Matrix-Initial
Distance and area measurements
B1-building perimeter, area, height and cadastral information (property lines)
Building and Property Infrastructure
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Column 3, temporal resolution, identifies the amount of time that imagery data
can be used before updated imagery is necessary. This takes into account the timeliness
of the imagery for a given application. For instance, if distance and area measurements
are needed, then the imagery should be updated every one to five years at a minimum.
Table 4-4 is an example of the temporal, spatial, and spectral resolution columns.
Table 4-4: Minimum Resolution Requirements
3. Temporal 4. Spatial 5. Spectral1-5 years 0.25-0.5 m Pan-V-LiDAR
Minimum Resolution Requirements
Column 4 represents the minimum spatial resolution that is necessary for an
analyst to use for a particular application. For instance, a 1 x 1 meter spatial resolution
image will allow an analyst to determine information about features that are 2 x 2 meter
in size or larger. Spatial resolution is reviewed in Section 2.3 of the thesis. A 0.25-0.5
spatial resolution is the minimum requirement for this particular application.
Column 5, spectral resolution, is the minimum requirement for an analyst to
determine information from an image for distance and area measurements. Therefore, an
analyst can use panchromatic, visible, or LiDAR imagery to obtain distance and area
measurements. Multi-spectral imagery will not provide any additional information
necessary to make distance and area measurements. Radar will not provide high enough
spatial resolution to make distance and area measurements. Various parts of the
electromagnetic spectrum that an imagery system may ‘see’ include ultraviolet; visible
green, blue, and red; near infrared; mid-infrared; thermal infrared; radar; and laser. The
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spectral resolution capability of each remote sensing system varies. For example, the
sensor onboard the Quickbird satellite can ‘see’ ultraviolet; visible green, blue, and red;
near-infrared, and mid-infrared wavelengths of the electromagnetic spectrum. Quickbird
uses these wavelengths to produce imagery. An example of the spectral resolution is
shown in Table 4-4. The panchromatic (Pan) and visible (V) wavelengths are minimum
spectral resolution requirements for this application. LiDAR, indicated in italics, is a
recommendation added by the researcher. LiDAR was not included in the original
spectral resolution Jensen and Cowen table because it is not a minimum requirement.
LiDAR provides elevation analysis capability.
Column 6 lists imagery systems identified by the researcher that will meet or
exceed the minimum managerial temporal, spatial, and spectral resolution requirements.
Table 4-5 shows examples of the imagery systems in the matrix. An explanation of the
capabilities of each of these is included in Chapter 2 and in the imagery system key.
Table 4-5: Imagery System
6. Imagery System
Aerial PhotographyQuickbirdIKONOSOrbView 3/4SPIN 2.SPOT 5IRS-P5Landsat 4,5,7JERS-1RADARSATERS-1,2.
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Column 7 indicates notes about each imagery application. This column points out
any special circumstances for the use of the imagery. For instance, the application of
environmental hazards identification has comments in the notes column as shown in
Table 4-6. Column 6 identified the imagery systems that would fulfill requirements of
the application. Several imagery systems have multiple sensors that produce different
types of imagery. For instance IKONOS and Quickbird imagery systems produce both
panchromatic and multi-spectral imagery, and each type has differing characteristics of
spatial resolution. The spatial resolution requirement for the environmental hazards
application is 0.25-2 meters. IKONOS and Quickbird panchromatic imagery fulfill this
requirement, however IKONOS and Quickbird multi-spectral imagery do not. The notes
column indicates this information.
Table 4-6: Notes
7. Notes
Panchromatic only for IKONOS and Quickbird
Several imagery characteristics were initially considered, but not incorporated
into the final matrix. These characteristics include ease of use, image file size,
processing time, and cost per image. Ease of use, or a measure of how easy the imagery
is to work with, was not used in the final matrix because the training hours required
would vary widely from one person to another. Because there are many different formats
possible for imagery, file size was not an included characteristic. The researcher assumed
that the computer used by GeoBase personnel would be capable of handling any file sizes
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without difficulty. Processing time was not included in analysis because GeoBase
computers should have adequate processors to handle the imagery. Cost per image was
not included because this is subject to change, and there are many cost structures
depending on the format of the imagery. Also, in many cases, imagery at minimal cost is
available to government agencies. Availability of the imagery was moved to the imagery
system key because this information is not needed to make a decision about what type of
imagery to obtain for an application.
4.3.4 Imagery Decision Matrix - Final
The final format of the imagery decision matrix is shown in Appendix B. The
format of the initial imagery decision matrix was difficult to use, making it necessary to
create a more streamlined tool. The final imagery decision matrix maintains all of the
information of the initial matrix, while providing a more user-friendly organization.
Column 1, Applications, is now a combination of the applications and attributes
columns from the initial imagery decision matrix. A difficulty with the initial matrix was
that applications and attributes did not provide a perfect match. For instance, in the
initial imagery decision matrix, powerline detection was matched to U-1-general utility
line mapping and routing. New applications were developed using USGS classifications
(Jensen, 2000:413-416). In the final matrix, powerline detection is part of transportation,
airfield, communications, and utilities, a broader application heading. Some applications
were grouped under the following headings: Multiple Categories, Emergency Actions,
and General Topographic Data.
Table 4-7 shows the applications column of the final matrix. Combining the
applications and attributes into one column makes the matrix easier to read and use.
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Major categories identify features such as urban or built-up land, agriculture, rangeland,
forest, water, and wetland. Applications that fit multiple categories are listed at the end
of the matrix. Under each major category, applications are organized displaying
successively stricter imagery resolution requirements. For instance, in Table 4-7, urban
or built-up land has the lowest resolution requirements. Residential area delineation, the
next application, requires higher resolution imagery to fulfill requirements. Single-family
residential identification requires even higher resolution, and the next five applications
listed in Table 4-7 require the highest resolution. All applications in the imagery decision
matrix are grouped into one of four categories depending on the level of imagery
resolution required. The category code can also be seen in Column 5, imagery systems,
which will be discussed later in this section. The particular resolutions will be discussed
further with an explanation of each data column contained in the imagery decision matrix
shown in Appendix B.
The list of applications is not all-inclusive, yet provides a solid foundation of
applications for use in GeoBase. Several applications such as distance and area
measurements and community planning could be listed under different major categories.
They are listed once for simplicity and arranged under the most logical major category.
Column 2, Temporal Resolution, identifies the minimum requirement for
each application listed. The imagery decision matrix is shown in Appendix B. The data
in the imagery decision matrix corresponding to the applications shown in Table 4-7 will
be presented next. Table 4-8 is the temporal resolution for the applications shown in
Table 4-7. The temporal resolution shown represents the refresh time that managers of
imagery typically need to acquire new imagery to maintain current information. Note
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that the category level of a particular application is independent of the temporal
resolution for the application. Several category 4 applications suggest that a temporal
resolution of 1-5 years is necessary, while several others recommend a temporal
resolution of 5-7 years.
Table 4-7: Applications Column
1. ApplicationsUrban or built-up land identification
Residential areas delineatedSingle family residential identified
Facility elevationsCommunity planningPopulation estimationUrban sprawl
Imagery Decision Matrix
Distance and area measurements, building footprints
Column 3, Spatial Resolution, indicates information as explained in the initial
imagery decision matrix. Spatial resolution in Table 4-13 is shown for the applications in
Table 4-11.
Column 4, Spectral Resolution, contains information as discussed in the initial
imagery decision matrix. Table 4-14 contains the data for the applications shown in
Table 4-11.
Column 5, Imagery Systems, contains the category code for the imagery systems
according to USGS classifications. The category codes were created to make the matrix
easier to use. Each of these categories was determined by USGS spatial resolution
guidelines. Category 1 identifies the lowest capability, whereas Category 4 indicates the
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highest capability. Figure 4-15 indicates the category codes for the applications shown in
Table 4-11.
Table 4-8: Minimum Temporal, Spatial, Spectral Resolution; and Imagery Systems
5. Imagery2. Temporal 3. Spatial 4. Spectral Systems5-10 years 20-100 m V-NIR-MIR-Radar-M-LiDAR Category 15-10 years 5-20 m V-NIR-MIR-Radar-M-LiDAR Category 23-5 years 1-5 m Pan-V-NIR-MIR-M-LiDAR Category 31-5 years 0.25-0.5 m Pan-V-LiDAR Category 4
1-5 years 0.25-0.5 m Pan-V-LiDAR Category 41-5 years 0.25-0.5 m Pan-V-LiDAR Category 45-7 years 0.25-5 m Pan-V-NIR-Radar-LiDAR Category 45-7 years 0.25-5 m Pan-V-NIR-Radar-LiDAR Category 4
Minimum Resolution Requirements
The minimum resolution requirements were identified as subjective depending on
the user’s experience, equipment and other conditions. For instance, fairly accurate
distance and area measurements are possible with 1-meter spatial resolution imagery
rather than the 0.25 to 0.5-meter spatial resolution suggested by the matrix (Frohn, 2002).
Instead of identifying all imagery systems that meet the requirements of each application,
an application is grouped into a category based on USGS classifications.
A single-source imagery recommendation for each application was not created.
In the initial matrix, an additional column was added for recommended imagery for each
application. This determination was based upon spatial, spectral and temporal resolution
requirements. A further consideration was to limit the number of imagery systems
represented to minimize the requirement on Air Force GIOs to obtain, manage, and
update a large number of imagery types. In the initial imagery matrix, government
imagery sources at minimal cost were given priority of selection over commercial sources
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of imagery. Therefore, if two imagery systems fulfilled the requirements the government
imagery system was recommended. Recommended imagery column is not presented in
the final imagery decision matrix to give the manager more choice in determining the
appropriate imagery for a specific application.
4.3.5 Imagery System Key - Initial
The Imagery System Key is used in conjunction with the Imagery Decision
Matrix to provide additional information on each imagery system. Two versions of an
Imagery System Key were developed. The initial Imagery System Key is presented here
to demonstrate the initial format and show how the key evolved. The initial Imagery
System Key is as a reference, whereas the final Imagery System Key is used by the
manager in deciding the final imagery system for a particular application.
The imagery decision matrix became too large to provide information in a user-
friendly format. A second table, the imagery system key, was developed to streamline
the imagery system matrix. The key also details the pertinent characteristics of each
imagery system. The imagery system key is shown in Appendix C.
The key has a spectral legend at the top explaining acronyms used throughout the
key and imagery system matrix. The spectral legend has a note at the bottom outlining the
use of italics in the matrix and key. Italics are used in the spectral resolution column,
indicating additional imagery system spectral resolutions that fulfill the requirements of
the application. For instance, for distance and area measurements, LiDAR will provide
information to make area measurements. This type of imagery was not presented in
Jensen and Cowen’s original table, but will be useful for this application. The spectral
legend is shown in Table 4-9.
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Table 4-9: Spectral Legend from Imagery System Key Table 2.
SPECTRAL LEGEND:
Imagery System Key-Initial
Pan=Black and white panchromatic(0.5-0.7µm)V=visible color(0.4-0.7µm)NIR=Near-infrared including black and white infrared, color infrared (0.7-1.1µm)MIR=Mid-infrared including black and white infrared, color infrared(1.5-2.5µm)
L=Light Detection and Ranging (LiDAR)Note: LiDAR is used mainly for elevation data; an additional imagery system such as Pan is used to meet the requirements of WSR-88D Radar=ground-based National Weather Service Radar=Aerial Radar
TIR=Thermal infrared(3-12µm)M=Multi-spectral (Includes V, NIR,MIR, and depending on sensor;
Note: Items in italics are above the minimum spectral resolution requirement, however, the imagery system chosen must also meet the minimum temporal and spatial resolution. Where Multispectral is not used in the table, it is not needed for the requirement.
Below the legend, six columns organize information about each imagery system
included in the thesis analysis. This additional information allows a user to see specific
details about each imagery system without encumbering the matrix use.
Column 1 has imagery systems listed according to subheadings of archived aerial
imagery, satellite imagery, multi-spectral aerial imagery, radar, LiDAR, and low spatial
resolution satellite imagery. The imagery systems are randomly ordered under each
subheading. The first few imagery systems, temporal resolution, and spatial resolution
are shown in Table 4-10.
Column 2 is the temporal resolution for each imagery system. This is how often
an imagery system can obtain images of the same geographic area on earth. Values range
from a few days to several weeks depending on the capability of the imagery system.
Some satellites such as IKONOS can obtain imagery of certain areas at customer request.
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The satellite will pass over a geographic region and pinpoint a specific area to image. If
tasked, IKONOS can obtain imagery of the same geographic area every two to three
days. Satellites such as Landsat-4, 5 Thematic Mapper cover a larger area with lower
spatial resolution. Landsat-4, 5 TM each cover the same geographic area every 16 days.
The revisit temporal resolution affects two issues, the availability of archived imagery
and the availability of on-demand satellite imagery. For instance, archived IKONOS
imagery for a geographic location was collected every two to three days. A manager can
request the imagery of a location and see if imagery is available. At best, an archive of
imagery may be available on the location that was collected by satellite every two to
three days. Additionally, if the manager wants to task a satellite to collect imagery for a
disaster situation, imagery is only available of the area every two to three days. That is
why in emergency situations, managers still need aerial imagery to collect the timely
information. As satellite technology progresses, the possibility of getting on-demand
imagery from satellite is approaching the temporal resolution for aerial imagery, which is
typically every 12 hours.
Column 3 indicates the spatial resolution for each imagery system indicated in
meters. When more than one number is listed, there are multiple sensors that obtain
imagery and Column 4 discussed next is also directly correlated to the information in
Column 3.
Column 4 contains the spectral resolution data for each imagery system. The
acronyms for spectral resolution are included in the legend at the top of the key.
Quickbird, for instance, obtains 0.61-meter panchromatic imagery and 2.5-meter multi-
spectral imagery. A more thorough discussion of spatial resolution and spectral
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Table 4-10: Imagery System, Revisit Temporal Resolution, and Spatial Resolution
Archived Aerial Imagery
Satellite ImageryQuickbird 1-5 days (depends on latitude) 0.61, 2.5IKONOS 2-3 days 1, 4OrbView-3,4 < 3 days 1, 4SPIN-2. 45 days 2, 10SPOT-1,2,3,4 26 days (1 day with 3 satellites) 10, 20
3. Spatial Resolution (m)
Varies depending aircraft altitude
1. Imagery System
Aerial Imagery, Pan or V
2. Revisit Temporal Resolution
12 hours
resolution is in Chapter 2. Table 4-11 has the spectral resolutions, availability, and
qualitative cost for the first few imagery systems.
Column 5 contains data on availability of imagery from each imagery system.
The availability of the imagery indicates where users can obtain the imagery. Websites
are provided. For some of the imagery systems, multiple sources are listed.
Column 6 contains cost information on each imagery system. The various levels
assigned to the cost data are minimal, low, medium, and high. Minimal cost (Min)
designation is used for imagery that is available to the Department of Defense from
agencies such as NIMA and USGS. There may be some minimal fees for obtaining the
imagery. Low cost refers to less than $2000 per image, while medium and high cost
refers to $2000 to $5000, and greater than $5000 respectively. Issues that will impact
cost include the availability of the imagery for a particular geographic area and the
requested imagery format. When more than one cost is listed, each cost corresponds to
the respective source in Column 5. Imagery listed as minimal cost (Min) may be only
accessible through certain contracts or after obtaining the proper license from the agency
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indicated. The costs are qualitative and are subject to change. These costs do not include
value-added products, such as human resource costs of inputting the imagery into the
GeoBase, tailoring the imagery to the specific application and maintaining the imagery in
the GeoBase.
Table 4-11: Spectral Resolution, Availability, and Qualitative Cost
Pan, M NIMA MinPan, M NIMA MinPan, M NIMA MinPan(panoramic), Pan NIMA MinPan, M NIMA Min
6. Cost(Qualitative)
Min
4. Spectral Resolution
Pan, V, NIR, MIR, TIR, M
5. Availability
USGS, Commercial
4.3.6 Imagery System Key - Final
The final format of the imagery system key involved several changes. The
imagery system key is shown in Appendix D. Several imagery systems were removed
from the final imagery system key for simplification. Specifically, MODIS, Spot-4
Vegetation, OrbView-2 SeaWiFS, AVHRR, GOES, METEOSAT, and NWS WSR-88D
were removed from the matrix. These imagery systems were associated mainly with
weather reporting and environmental changes on a regional or national scale. The spatial
resolutions of these imagery systems do not provide as much usefulness to urban and
suburban applications as the other imagery systems presented in the key.
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The spectral legend of the imagery system key was updated to incorporate
wavelengths in the electromagnetic spectrum. The updated spectral legend is shown in
Appendix D.
Column 1 of the imagery system key contains category codes, based on low to
high spatial resolution, for each imagery system. For aerial platforms, such as Daedalus,
the imagery may be category 3 or category 4 depending on the altitude of the aircraft. At
a lower altitude, the imagery will have higher spatial resolution making it a category 4.
Column 1 of the key can be a quick reference as to the capabilities of each imagery
system. Column 1 categories link back to the final imagery decision matrix, Column 5.
The manager has the choice of determining the imagery system for his application with
the appropriate category code. This is mainly based on spatial resolution; however each
imagery system should still be examined to identify additional system details. Table 4-12
details the category code, imagery system, spatial resolution, and spatial resolution for
the first few imagery systems.
Columns 2, 3, and 4 contain similar data as outlined in the initial imagery system
key. The column numbers are shifted and some formatting changes are made to the
display of information.
Column 5, spectral resolution, has a more significant change in formatting. Some
imagery systems have multiple bands covering different portions of the mid-infrared,
near-infrared, or thermal-infrared segments of the spectrum, such as eight bands from
ultraviolet to near-infrared and two thermal-infrared bands for Daedalus. Table 4-13
shows the information for the first few imagery systems presented in Table 4-12.
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Column 6 and 7 contain similar data about the availability and cost respectively of
each imagery system outlined in the initial key. Column 8 is a general column added at
the far right side of the key. Imagery systems are grouped according to researcher-
Table 4-12: Imagery Category, Imagery System, Temporal Resolution, and Spatial Resolution
Aerial Imagery
LiDAR
On Demand weather permitting
0.2 horizontal, 0.1 vertical
On Demand weather permitting
2.5 at 1000 m AGL
ATLAS(aerial)
On Demand weather permitting
2.5 at 6000 ft AGL
Aerial Imagery
On Demand weather permitting
Depends on aircraft altitude
Daedalus(aerial)
2. Imagery System3. Temporal Resolution
4. Spatial Resolution (m)
4
1. Imagery Category-
Range 1 to 4 (low to high)
4
3 or 4 depends on altitude
3 or 4 depends on altitude
defined geographic area coverage: precision, local, or regional. These are qualitative
measures that indicate how the imagery system can be used. Precision denotes the
highest spatial resolution of <2 meters and would allow decisions to be made about a
single base facility. Local represents a resolution of 2-15 meters, displaying a broader
view of an Air Force base. Regional, with the least spatial resolution capability, >15
meters, would encompass an area as large as a county or state.
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Table 4-13: Spectral Resolution, Availability, Cost and General Use
6. Availability
7. Cost(Qualitative) 8. General Use
L (Laser)
5. Spectral Resolution
Pan, V, NIR, MIR, TIR, MM (8 bands from UV to NIR, and 2 TIR bands)
M (6 bands in V and NIR, 2 MIR, 6 TIR)
USGS, CommercialLow, High
Precision measurements and analysis
NIMA possible (http://www.spatial.maine.edu)
Commercial HighPrecision measurements and analysis
Low, High
Precision measurements and analysis
NASA Stennis Space Center(http://wwwghcc.msfc.nasa.gov) Med
Precision measurements and analysis
4.4 Validation
Validation involves two procedures: Geo-Integration Officers evaluating the
decision tools, and acquiring imagery that could be used in GeoBase. The validation tests
the analysis and procedures proposed by the research. An appropriate breadth and depth
of validation is necessary to determine if the analysis and procedures are sound. The end
product of this research is the imagery decision matrix, imagery system key, instruction
sheet, and procedures for obtaining imagery for Air Force applications. The instruction
sheet provides step by step instructions for GIOs to use the matrix and key. The
instruction sheet is included in Appendix E.
Three Air Force bases were selected to validate these products and procedures
based on the variability of missions, geographic location, Major Command (MAJCOM),
background and level of experience of participating validators, and level of GeoBase
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implementation at the Air Force base. More specific information on each validation base
and validator follows.
Geo-Integration Officers from the United States Air Force Academy, Elmendorf
Air Force Base, and Wright-Patterson Air Force Base concurrently validated the imagery
decision matrix, imagery system key, and an instruction sheet. The researcher
determined if the process of obtaining imagery for the three sample Air Force bases was
plausible and practical. The validation determines the usefulness of the matrix and the
researcher records suggestions, benefits and difficulties the GIOs had from using the
matrix, key, and instruction sheet.
4.4.1 United States Air Force Academy, Colorado
Comments were positive from the USAFA Chief of Geospatial Information and
Services Center. The GIO found the matrix to be very useful and a tool that presents a
variety of new options (Portillo, 2003). He indicated that he would use the matrix for
future decisions regarding imagery implementation. Although some GIOs are not using
the latest satellite imagery, he currently is using IKONOS satellite imagery and aerial
imagery.
The initial imagery decision matrix, initial imagery system key, and instruction
sheet were sent to GIOs for validation. The initial imagery decision matrix included a
column for recommended imagery systems rather than a category of systems in the final
imagery decision matrix. The GIO was interested in the criteria used to determine the
recommended imagery. The researcher clarified the three selection criteria: fulfillment
of resolution requirements, minimizing inventory, and low cost priority. After
consideration, the recommended imagery was removed from the final matrix. This
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column of information was not presented because a more thorough understanding of the
nuances of each imagery system is required to make this determination and each manager
can best decide the particular imagery system for a given problem. By presenting the
systems by category code that meet the requirements, the manager then can select the one
that best meets their specific requirements.
The GIO commented that an experienced GIS specialist most easily uses the
matrix. Another suggestion by the GIO was to merge multiple imagery sources to create
a product. The GIO used this method in the past with SPOT 5 imagery and Quickbird
imagery. The SPOT 5 imagery was used for the majority of the coverage, with Quickbird
imagery merged at specific areas that require higher spatial resolution. This method is
recommended in the future research section of this document as an alternative imagery
product for applications in GeoBase. Also, the GIO was aware of imagery at minimal
cost from NIMA and how to access it. NIMA-archived imagery of the Air Force
Academy is noted later in this section.
4.4.2 Elmendorf Air Force Base, Alaska
The Elmendorf AFB GIO found the matrix and key to be easy to follow with the
supplemental instruction sheet. The temporal resolution indicated in the matrix was
appropriate based on his experience. Imagery recommendations were evaluated as good.
The GIO plans on keeping the matrix in a “tool box” as a useful reference for future
imagery application decisions (Brown, 2003).
One drawback to the matrix, according to the GIO, was that three-spectral band
color imagery was not clearly indicated. Based on this feedback, first imagery system,
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aerial imagery, was updated in the final matrix to indicate that panchromatic and visible
color imagery is included.
The GIO expressed that he had difficulty obtaining imagery from NIMA in the
past. He was in the process of seeking $10,000 to acquire imagery needed for the base.
The researcher obtained imagery from NIMA through the Commercial Satellite Imagery
Library and sent the imagery to the Elmendorf GIO.
The GIO had difficulty using one of the websites suggested for obtaining NASA
imagery. The researcher provided the GIO with a more direct website.
http://edcdaac.usgs.gov/aster/aster_pricing.html. NASA imagery is archived by the
USGS EROS Data center and is available by request. This process is further discussed in
Section 4.4.4.
4.4.3 Wright-Patterson Air Force Base, Ohio
A team member from the Wright-Patterson GIO determined that the matrix was
“an interesting compilation of the various technologies being used for satellite imagery”
(Castaneda, 2003). Wright-Patterson is still in the developmental stage of inputting their
data and imagery into a GeoBase. Satellite imagery will be incorporated later for specific
applications once the initial GeoBase is operable. The GIO plans on using the matrix for
implementing satellite imagery into GeoBase.
The GIO suggested applying the matrix to GeoReach activities where quick-turn
imagery is necessary. Careful consideration was given to this issue; however, a separate
analysis of the appropriate imagery systems is necessary for unique wartime situations.
This will be discussed in the future research section of this thesis, Section 5.4.
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The GIO recommended that on-demand aerial imagery be included in the matrix
as a benchmark for cost and applications. He noted that, in the past, Wright-Patterson
AFB has mainly used on-demand aerial imagery for its GIS. If the cost and applications
of on-demand aerial imagery were integrated into the matrix, this could be used as a
comparison to archived satellite imagery.
Satellite imagery is lower in cost. As satellite imagery progresses in spatial,
spectral, and temporal resolution; satellite imagery may replace the need for aerial on-
demand imagery. The on-demand aerial imagery is incorporated into the imagery system
key, under Daedalus, a commercial aerial imagery system. Each decision-maker will
have to determine the cost for obtaining on-demand aerial imagery for their needs and
geographic region. Providing a specific dollar value for obtaining on-demand aerial
imagery could be misleading due to the variability of each situation. Therefore, specific
dollar values are not included in the imagery system key.
4.4.4 Imagery Procurement
Validation is used to determine whether the processes suggested by the research
are plausible and practical. The process of procuring imagery was an important
undertaking of this research. Information was obtained indicating that a wide range of
imagery is available to Department of Defense organizations at minimal cost through
NIMA (Commercial Imagery Copyright & License Interim Guidance, 2002). Another
major source of imagery is the USGS. The process of obtaining imagery from USGS was
investigated, however NIMA was chosen as the primary imagery source for this research
because of its extensive commercial inventory. Imagery for the three validation bases
was available from NIMA. Imagery was requested for Elmendorf Air Force base.
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A document titled, Commercial Imagery Copyright and License Interim
Guidance, details the Unclassified commercial imagery available through NIMA (2002).
According to this document, as stated in Department of Defense (DoD) Directive
(DoDD) 5105.60, NIMA will “serve as the sole DoD action agency for all purchases of
commercial and foreign government-owned imagery-related remote sensing data by the
DoD Components (and) serve as the primary action agency for such purchases by any
other Federal Department or Agency, on request” (2002). NIMA will provide imagery at
minimal cost that it has acquired to any United States government entity with priority
support given to the Intelligence community and State Department customers. The
documentation is set to provide imagery to DoD Agencies; however the process to
distribute the imagery does not work well.
The process of obtaining imagery for the three validation Air Force bases proved
more difficult than anticipated. Imagery is easily accessible to government agencies that
are part of the intelligence community, such as the National Air Intelligence Center
(NAIC), and the Central Intelligence Agency (CIA). Contact was made with eight
personnel at NIMA including the imagery program directors in Virginia and St. Louis
offices, and no one provided a solution. Without access to an Internet connected Secret
or Top Secret computer system, the NIMA Commercial Satellite Imagery Library (CSIL)
is unavailable to Department of Defense organizations. A user also would need a license
granted by NIMA to use the imagery.
Many lessons were learned while attempting to obtain Unclassified commercial
imagery from NIMA. Imagery is available at minimal cost to U.S. government agencies
via Secret or Top Secret networked computers. With the help of an intelligence agency
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employee with Secret or Top Secret Internet access, this imagery can be procured. Once
an organization searches the CSIL and submits an order, a compact disc containing the
imagery is sent by mail. The imagery is purchased by NIMA as needed from commercial
sources and made available to government agencies. Special requests can be made for
imagery if it is not available in the CSIL, but the requestor may incur an additional cost.
The Intelligence community has priority when making imagery requests. Ordering
imagery via Unclassified Internet was a NIMA initiative that was planned and then
delayed as a consequence of September 11, 2001.
Geographic coordinates are required when searching for imagery in NIMA’s
database. The geographic coordinates for the three bases were found on several websites
using an online search engine. Coordinates were input to the Commercial Satellite
Imagery Library (CSIL) on a Top Secret computer system. A user can either type a
center point for the geographic location or input the upper left and lower right corner
coordinates, creating the rectangular area of coverage needed. The search can be
narrowed using several parameters including dates of acquisition, satellite track, country
imaged, maximum cloud cover percentage, and specific imagery systems.
Imagery was located for each of the three validation bases using center search point
coordinates. Through the CSIL, Landsat-5 TM10, Landsat-7 ETM+, and IKONOS-1, 2
multi-spectral, provided coverage for Wright-Patterson AFB. Landsat-7 ETM+, Landsat-
5 TM, RADARSAT-1, and Landsat-5 imaged the Air Force Academy. SPOT-1
panchromatic, RADARSAT-1, and IKONOS-1 panchromatic and multi-spectral covered
Elmendorf AFB. Imagery for Elmendorf AFB was downloaded from NIMA.
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Figure 4-2 is an IKONOS 1-meter spatial resolution, black and white panchromatic of
Elmendorf AFB collected in year 2000. Commercial and residential areas are visible in
the image as well as main and side roadways. With a 17” computer monitor, cars can be
distinguished traveling on the roadways. This data from this image can be used for land-
use planning, distance and area measurements, and pre-emergency imagery to name a
few.
Figure 4-2: IKONOS 1-Meter Black and White Panchromatic (NIMA, 2003)
Figure 4-3 is an IKONOS 4-meter near-infrared band from a multi-spectral collection
in 2000. The image shows the golf course area of Elmendorf AFB. The long narrow
brighter strips are the golf course fairways. Because healthy vegetation reflects near-
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infrared energy more than stressed vegetation, the fairways of the golf course appear
brighter than the surrounding vegetation. The IKONOS imagery for Elmendorf AFB was
provided to the Elmendorf GIO. He plans to use the imagery for base planning and
environmental analysis.
Figure 4-4: IKONOS 4-Meter Near-Infrared Band from Multi-Spectral Imagery (NIMA, 2003)
Issues of concern when obtaining imagery for each validation Air Force base were
cloud cover, availability of the imagery, and format compatibility of the imagery with
GIS software programs. Cloud cover is usually not a problem with commercial imagery
provided by NIMA because the imagery is produced to sell in an open market. This
commercially accepted imagery is provided to government agencies at minimal cost. The
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availability of imagery has not been a problem with imagery from the past two or three
years available from NIMA. Geo-referenced data is the standard provided by NIMA on
compact disc or through an Internet download (Bignell, 2003). This geo-referenced data
is provided in a format that is compatible with Geographic Information Systems (GIS)
software such as ESRI ArcView 8.1. ArcView 8.1 is frequently the GIS software used by
Air Force bases.
The USGS is also a source of imagery. ASTER imagery is available at minimal cost
to DoD by Internet request. The website is http://edc.usgs.gov/. According to personnel
at the USGS, Earth Resources Observation Systems (EROS) Data Center, a request form
for imagery must be filled out. Then an email is sent to the requester to notify whether or
not the imagery is approved for release. The process may take a few weeks.
4.4.5 Imagery Decision Flow Chart
A flow chart was envisioned as an alternative to the matrix, key, and
supplemental instruction sheet. A preliminary flow chart will be included in Chapter 5 as
a recommendation for future development. The flowchart could be used as a foundation
for developing a software program. The ideas for the software program will be discussed
further in Chapter 5 in the future recommendations section.
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V. Conclusions
5.0 Introduction
An imagery decision matrix, imagery system key, and instruction sheet were
developed from research of civilian and government applications of remote sensing
imagery. These products were validated with GeoBase personnel at three diverse Air
Force bases. The process of obtaining imagery from NIMA was detailed, and these
products were made available to the GIOs from the three validation bases.
5.1 Results
The imagery decision matrix, imagery system key, and instruction sheet provide a
useful guidance tool to GeoBase personnel. The spreadsheet format of the matrix and
key combined a decision tool with satellite imagery information into one resource. The
instruction sheet provides easy to follow steps on using the matrix and key.
The matrix addressed many Air Force mission requirements including
infrastructure, natural resource management, and command and control. A wide range of
imagery applications was presented in the matrix. The applications are relevant to
peacetime operations at Air Force bases.
GeoBase personnel at three Air Force bases validated the matrix, key, and
instruction sheet. They made recommendations for improvement and provided feedback
about the usefulness of these tools. Their recommendations and comments were
incorporated into the final version of the matrix, key, and instruction sheet. The result of
their participation in the research is a more precise tool for use by GeoBase personnel.
5-2
Rather than provide the imagery for a specific application to the validation bases,
the imagery available at NIMA was offered to the three validation base GIOs. The Air
Force Academy GIO had previously acquired imagery for his base requirements from
NIMA and commercial sources. Therefore he did not need the imagery that was
identified at NIMA. The Wright-Patterson GIO decided it would be best to determine
exactly what imagery the base could use before proceeding with a request of imagery.
Wright-Patterson AFB is in the early phases of implementing GeoBase, and the GIO
wanted to make a full evaluation of their needs before requesting imagery from NIMA.
The Elmendorf GIO requested the IKONOS imagery to use for environmental and
infrastructure planning at the base. Obtaining the imagery was more difficult than
expected. Due to time constraints the imagery was made available to the Elmendorf GIO,
but there was no time to receive feedback from the GIO concerning implementation of
the imagery for environmental and infrastructure planning.
Imagery of three validation bases was successfully accessed from a classified
government computer system with the help of an employee from the National Air and
Intelligence Center (NAIC), Wright-Patterson Air Force Base.
5.2 Summary
The U.S. Air Force is in the process of implementing GeoBase, a geographic
information system (GIS), throughout its worldwide installations. The Air Force has GIS
needs that can be augmented by readily available imagery. An extensive survey and
compilation of imagery applications provides decision-makers focused with focused
information. Incorporating various imagery types will significantly increase GeoBase
usefulness for a range of mission requirements. Potential Air Force uses of imagery
5-3
include identifying heat loss, environmental monitoring, command decision-making, and
emergency response.
Findings from current literature, results from an Air Force Geo-Integration Office
(AF/GIO) survey, and insights from a Geospatial Technologies Symposium, were used to
develop a comprehensive imagery applications list that satisfies Air Force mission
requirements. An imagery decision matrix was developed that allows users to select an
application and identify the most ideal imagery for accomplishing the task. Potential uses
of various satellite and aerial imagery capitalize on the technological innovations over the
past few years, a tremendous opportunity for GeoBase enhancement. A supplemental
imagery system key provides further details of each imagery type. Geo-Integration
personnel at three Air Force bases evaluated the matrix. Their feedback strengthened the
decision matrix tool and provided a valuable exchange of ideas regarding implementation
of multiple imagery types in GeoBase. The products of this thesis effort will help
decision makers enhance the capabilities of their GeoBase, saving time, increasing
efficiency, and creating continuity.
An increased awareness of the possibilities of GIS, when enriched by imagery,
and the efficiency afforded by the matrix, greatly reduces time required by GeoBase
personnel to identify and implement the optimum imagery. The gap is bridged between a
simple GIS and a multi-faceted GeoBase with guidance presented in the matrix.
GeoBase personnel have the opportunity to exploit the capabilities afforded by
commercial satellite imagery. An enhanced GIS can include multiple types of imagery.
A simple GIS may have only one type of image such as a black and white panchromatic.
5-4
By incorporating multiple types of imagery, decision makers have a powerful analysis
tool to make more informed decisions.
This thesis effort tapped imagery from NIMA, which is available through its
online Commercial Satellite Imagery Library. Presently, this library is only available
through classified internet access. The NIMA imagery can be obtained for a minimal
cost, with the assistance of intelligence personnel at the base level.
These research results have the potential to dramatically increase the capability of
GeoBase. The matrix serves as a practical guide for decision-makers to determine the
most appropriate imagery for a given application. All three validators of the matrix, key,
and instruction sheet plan to use these tools when making imagery decisions for the Air
Force.
5.3 Limitations
Some limitations were identified with regard to the usability of the matrix, key,
and instruction sheet; the imagery procurement process; and the applications focus. One
of the challenges of making the matrix, key, and instruction sheet is how to determine an
appropriate level of detail. During the process of constructing these products, the
researcher made the assumption that the intended users of the matrix, key, and instruction
sheet would be GIOs. Experienced GIS personnel are the preferred users of the imagery
decision matrix, imagery system key, and instruction sheet. A substantial working
knowledge of GIS and remote sensing imagery is recommended for successful
understanding and implementation of the matrix.
Most of the applications presented in the matrix relate to Civil Engineering
operations. Applications detailed in current literature emphasize environmental and
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infrastructure management projects. These types of applications are typically the
responsibility of the Air Force Civil Engineer. As more research on diverse applications
becomes available, the matrix can be updated to incorporate the applications of other
career fields. With remote sensing technology advances and continued innovations in the
practical use of imagery, the GeoBase applications scope will broaden.
When base-level GIOs attempt to obtain imagery, they may encounter some
limitations. These issues are best illustrated by an example: A GIO may require 1-meter
or better spatial resolution satellite imagery collected in 2002. The GIO intends to use
this imagery for distance and area measurements and elevations of all base facilities.
Several new buildings have been constructed since 2000. When the GIO makes a request
from NIMA, the most current imagery available that fulfills the 1-meter requirement was
collected by IKONOS satellite in 1999. Although the spatial resolution meets the GIO’s
requirements, the imagery is not current enough to reflect the newer base facilities. The
imagery would also need to be cloud-free and properly formatted for GeoBase
compatibility. In this case, the GIO has two options. Make a special request to NIMA
for more current imagery to fulfill the requirement or obtain the imagery through another
source. Special requests to NIMA may require funding from the GIO’s Air Force base;
however if several organizations request the imagery, the cost may be split among them.
5.4 Future Research
Many opportunities remain to be studied as they apply to GeoBase. Future
research related to this thesis effort should focus on several issues: improving usability of
the matrix, key, and instruction sheet; implementation of the imagery into GeoBase;
widening the applications focus to encompass all Air Force squadrons on a typical base;
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exploring additional imagery types, merging imagery, acquiring imagery, and wartime
imagery applications.
Further development into the usability of the matrix, key, and instruction sheet
would enable wider use of the decision making tool. One of the limitations of the matrix,
key, and instruction sheet is that a high-level of GIS experience is needed to fully
understand and make decisions based on the information provided. By reading the thesis,
most users without prior GIS experience should gain enough background knowledge to
use the matrix, key, and instruction sheet effectively. As more individuals incorporate
the decision tool into daily operations, adjustments should be made so people with
minimal GIS competence can use it. Another idea is to develop a separate software
program that assimilates all of the information from the key, matrix, instruction sheet,
and provides additional information when requested by the user. An option considered
for presenting the information was the Microsoft Visual Basic software application.
Visual Basic could be used to build an interactive Microsoft Windows imagery
decision software program. Visual Basic uses a Microsoft Windows graphical user
interface. The beginning stage of this software program is illustrated in an imagery
decision flowchart, included in Appendix F of this document.
To use the flowchart, start at the upper left side at spatial resolution. Determine
the spatial resolution requirement for a specific application and either progress to the
spectral resolution or proceed to the next page for higher spatial resolution. If >20-meter
spatial resolution fulfills the requirement, next determine the spectral resolution
requirements. The first three spectral resolutions are organized from left to right in
increasing spectral resolution capability. If panchromatic imagery (Pan) fulfills the
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spectral resolution requirement, Landsat-7 is the recommended imagery system. If not,
the subsequent spectral resolutions listed are multi-spectral without thermal-infrared
capability (MS no TIR), multi-spectral with thermal-infrared capability (MS w/ TIR),
Elevations, and Cloud covered. Each of these indicates a recommended imagery system
that fulfills the requirement. Multi-spectral and thermal-infrared applications are detailed
in Chapter 2 and in the final imagery decision matrix. LiDAR is typically used if the
application is to obtain elevations and area measurements for natural resources. Radar is
typically used for geographic areas that have constant cloud cover. Several external
considerations may be built into the program, including an imagery procurement
timeline, imagery updating frequency (temporal resolution), and imagery acquisition
budget.
This flowchart and subsequent software program is an idea that will need further
development. With software programming, the decision tool could link to imagery
examples for each imagery system. The software program could have an Internet link to
an Unclassified NIMA commercial imagery website if it becomes available. Internet
links could also connect to additional sources of imagery such as USGS. The flowchart
assumes that each decision is independent of the others. A GIS professional would likely
make an imagery decision accounting for all variables at the same time. Spatial, spectral,
and temporal resolution; timeline; frequency; and budget are dependent variables that
affect the final decision of which imagery source to choose.
Implementation of the imagery into GeoBase will allow researchers to determine
the extent of capability gained by use of additional imagery types. Research is needed
that addresses appropriate amount and type of information made available to users to
5-8
effectively and efficiently complete their work. Too much data in the GeoBase may
make maintaining the GeoBase difficult. While there are many capabilities afforded by
imagery enhancement of GeoBase, GIOs have to be selective when acquiring and
maintaining imagery for their requirements.
Wider validation of the decision tool presented in this research is needed. The
validation should encompass horizontal and vertical integration on each Air Force base.
Horizontal integration with Intelligence, Security Forces, and Communications
Squadrons, to name a few, will generate mission-specific uses of the matrix. Air Force
base GIOs should partner with intelligence squadron personnel at base level to obtain
imagery from NIMA. Improved continuity in terms of information sharing will be
possible with wider use of a common imagery decision tool.
Vertical integration of the matrix will provide personnel with a tool, which will
facilitate communication regarding GeoBase imagery requirements and solutions. By
using a common decision analysis tool, improvements and updates can be made that will
benefit users of GeoBase.
Exploration of imagery merging, imagery acquisition, and additional types of
imagery is needed. Imagery merging is taking more than one imagery source and
combining them to make a composite image. This method is used to reduce the cost and
time of acquiring imagery at higher resolution for an entire geographic area. A higher
resolution may only be required for small parts of a geographic area of interest. Often,
archived imagery of a large geographic area is merged with more current higher-
resolution imagery. A merged imagery product reduces the time and cost associated with
5-9
replacing all of the imagery of an entire geographic area with a costlier, higher-resolution
product.
Exploration of additional imagery sources and their implementation into GeoBase
is needed. There are many sources of imagery that may enable GeoBase personnel to
inexpensively enhance their GIS. Additional emphasis on acquiring imagery from new
commercial sources as they become available will provide the GeoBase with the most
current and capable imagery.
New computer systems coming on line at the base level such as Theater Battle
Management Core System (TBMCS) have SIPRnet Secret internet connection capability.
Squadron Commanders can obtain access to NIMA imagery at the Commercial Satellite
Imagery Library (CSIL) through SIPRnet-connected computer systems. This may be the
best way to obtain Unclassified imagery for GeoBase use until the CSIL is made
available on Unclassified Internet-connected computers. However, the most compelling
reason that imagery should be made available through Unclassified access is that
Unclassified imagery accounts for a large percentage of the inventory. One source at
NIMA noted that 900,000 satellite images were available from Space Imaging, Inc.
through NIMA’s Commercial Satellite Imagery Library (CSIL). Only a few satellite
images may be necessary to provide full coverage of an Air Force base.
The GeoBase concept, when used in a deployment, is known as GeoReach.
GeoReach database requirements are similar to those for GeoBase, with additional
contingency requirements. Personnel working in a deployed location should have access
to real-time classified imagery. The real-time classified imagery may provide additional
capabilities to deployed GIOs. This research may be applied to GeoReach with
5-10
contingency requirements taken into account, such as the necessity for real-time
classified imagery, and the need to collect aerial imagery at a higher altitude. Real-time
classified imagery may be required in situations where the features in the geographic
environment are changing rapidly. It may be possible to track the enemy positions
through real-time classified imagery to gain advantages over the enemy. Aircraft are at
risk of attack when flying within range of surface-to-air (SAM) missiles and anti-aircraft
artillery (AAA) in a war zone. For this reason, war zone remote sensing aerial platforms
need to operate at a much higher altitude than for peacetime collection.
Imagery applications that would greatly improve the capability of GeoBase
include chemical plume detection and asbestos identification, but these require further
exploration into new types of imagery. Chemical plume detection and mapping is an area
of interest in cases of environmental monitoring and disaster management. The ability to
track the plume of chemicals would enable commanders to make more informed
decisions regarding safety and cleanup. Asbestos identification through the use of
advanced imagery would enable airmen to determine those facilities that have specific
requirements regarding asbestos. Once the facilities that have asbestos are identified,
appropriate action can be taken to encapsulate the asbestos or remove it properly. Hyper-
spectral imagery may be the next step in fulfilling these challenging requirements.
Hyper-spectral imagery expands upon the capabilities provided by multi-spectral
imagery. Hyper-spectral imagery is made up of hundreds of spectral bands while multi-
spectral typically has less than a dozen. The higher number of spectral bands allows the
electromagnetic spectrum to be split up into smaller sections, thereby creating greater
precision when distinguishing features in an image. Hyper-spectral imagery was not
5-11
included in this thesis because of time constraints. The decision was made to focus on
panchromatic and multi-spectral imagery; and exclude hyper-spectral imagery.
GeoBase can greatly benefit from the multi-spectral, LiDAR, and radar imagery
systems. By focusing on these imagery systems, a greater degree of depth can be
presented without the need to introduce the complexity of hyper-spectral imagery at this
early stage of GeoBase implementation. Additional research is needed on hyper-spectral
imagery to determine possible applications. Hyper-spectral imagery may provide the
capability to detect chemical plumes (Frohn, 2002).
A-1
Appendix A: Initial Imagery Decision Matrix
This appendix contains the initial imagery decision matrix. The matrix has 7
columns of data to help decision-makers determine the appropriate imagery for a
particular application. A more detailed discussion of each column is in Chapter 4.
Jensen’s textbook, Jensen and Cowen’s journal article, and a NIMA document
were used as references for the imagery decision matrix and imagery system key. They
are as follows:
Jensen, John R. Remote Sensing of the Environment An Earth Resource Perspective. Upper Saddle River, New Jersey: Prentice-Hall Inc., 2000.
Jensen, John R. and Dave C. Cowen, “Remote Sensing of Urban/Suburban Infrastructure
and Socio-Economic Attributes*,” Photogrammetric Engineering & Remote Sensing, 65: 611-622 (May 1999).
“Status of Selected Current & Future Commercial Remote Sensing Satellites.” National
Geospatial Intelligence School, NIMA. Handout from a seminar. 24 June 2002.
A-2
Tabl
e 1.
1. A
pplic
atio
ns2.
Attr
ibut
es3.
Tem
pora
l4.
Spa
tial
5. S
pect
ral
6. Im
ager
y Sy
stem
7. N
otes
Land
Use
/Lan
d C
over
L1-U
SGS
Leve
l I5-
10 y
ears
20-1
00 m
Aeria
l Pho
togr
aphy
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
SPIN
2.
SPO
T 5
IRS-
P5La
ndsa
t 4,5
,7JE
RS-
1R
ADAR
SAT
ERS-
1,2.
L2-U
SGS
Leve
l II
5-10
yea
rs5-
20 m
Aeria
l Pho
togr
aphy
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
SPIN
2.
SPO
T 5
IRS-
P5La
ndsa
t 7-P
an o
nly
Rad
arL3
-USG
S Le
vel I
II3-
5 ye
ars
1-5
mAe
rial P
hoto
grap
hyQ
uick
bird
IKO
NO
SO
rbVi
ew 3
/4SP
IN 2
.SP
OT
5IR
S-P5
L4-U
SGS
Leve
l IV
1-3
year
s0.
25-1
mAe
rial P
hoto
grap
hyQ
uick
bird
IKO
NO
SO
rbVi
ew 3
/4
Imag
ery
Dec
isio
n M
atrix
-Initi
al
Pan-
V-N
IR-
MIR
-M-L
iDAR
Pan-
M-L
iDAR
Min
imum
Res
olut
ion
Req
uire
men
ts
V-N
IR-M
IR-
Rad
ar-M
-
V-N
IR-M
IR-
Rad
ar-M
-
A-3
1-5
year
s0.
25-0
.5 m
Pan-
V-Li
DA R
Aeria
l Pho
togr
aphy
Dig
ging
Per
mits
Com
mun
ity
plan
ning
Build
ing
foot
prin
tsSp
ot h
eigh
tsFa
cilit
y el
evat
ions
3-di
men
sion
al
rend
erin
gFa
cilit
y m
anag
emen
tTr
ansp
orta
tion
Infr
astr
uctu
reT1
-gen
eral
road
cen
terli
ne1-
5 ye
ars
1-30
mPa
n-V-
NIR
-MAe
rial P
hoto
grap
h yQ
uick
bird
IKO
NO
SO
rbVi
ew 3
/4SP
IN 2
.SP
OT
1,2,
3,4,
5IR
S-P5
Land
sat 7
ETM
JER
S-1
T2-p
reci
se ro
ad w
idth
1-2
year
s0.
25-0
.5 m
Pan-
VAe
rial P
hoto
grap
hy
Traf
fic s
tudi
es5-
10 m
inut
es0.
25-0
.5 m
Pan-
VAe
rial P
hoto
grap
hy
T4-p
arki
ng s
tudi
es10
-60
min
utes
0.25
-0.5
mPa
n-V
Aeria
l Pho
togr
aph y
Util
ity In
fras
truc
ture
Pow
erlin
e de
tect
ion
1-5
year
s1-
30 m
Aeria
l Pho
togr
aphy
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
Pan-
V-N
IR-M
-Li
DAR
U1-
gene
ral u
tility
line
m
appi
ng a
nd ro
utin
g
Dis
tanc
e an
d ar
ea
mea
sure
men
ts
Tran
spor
tatio
n m
anag
emen
t
B1-b
uild
ing
perim
eter
, are
a,
heig
ht a
nd c
adas
tral
info
rmat
ion
(pro
perty
line
s)
Bui
ldin
g an
d Pr
oper
ty
Infr
astr
uctu
re
T3-tr
affic
cou
nt s
tudi
es (c
ars,
ai
rpla
nes,
etc
.)
A-4
SPIN
2.
SPO
T 1,
2,3,
4,5
IRS-
P5La
ndsa
t 7 E
TMJE
RS-
11-
2 ye
ars
0.25
-0.6
mPa
n-V-
LiD
ARAe
rial P
hoto
grap
hy
1-2
year
s0.
25-0
.6 m
Pan-
LiD
ARAe
rial P
hoto
grap
hy
Earth
wor
kD
1-la
rge
scal
e D
EM5-
10 y
ears
0.25
-0.5
mPa
n-V-
LiD
ARAe
rial P
hoto
grap
hyH
ighw
ay a
lignm
ent
Coa
stal
stru
ctur
e de
sign
Soil
eros
ion
mod
elin
g
Dra
inag
eH
ydro
logy
m
odel
ing
Con
tour
ge
nera
tion
D2-
larg
e sc
ale
slop
e m
ap5-
10 y
ears
0.25
-0.5
mPa
n-V-
LiD
ARAe
rial P
hoto
grap
hySo
cioe
cono
mic
Cha
ract
eris
tics
Popu
latio
n es
timat
ion
S1-lo
cal p
opul
atio
n es
timat
ion
5-7
year
s0.
25-5
mAe
rial P
hoto
grap
hyU
rban
spr
awl
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
SPIN
2.
SPO
T 5
IRS-
P5
Pan-
V-N
IR-
Rad
ar-L
iDAR
Dig
ital E
leva
tion
Mod
el
(DEM
) Cre
atio
n
U2-
prec
ise
utilit
y lin
e w
idth
, rig
ht-o
f-way
U3-
loca
tion
of p
oles
, m
anho
les,
sub
stat
ions
Floo
d pr
even
tion
and
plan
ning
/buf
fer
A-5
5-15
yea
rs5-
20 m
Aeria
l Pho
togr
aphy
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
SPIN
2.
SPO
T 5
IRS-
P5La
ndsa
t 7 E
TMQ
ualit
y of
life
an
alys
isS3
-qua
ltiy
of li
fe in
dica
tors
5-10
yea
rs0.
25-3
0 m
Pan-
V-N
IR-
Rad
ar-L
iDA R
Aeria
l Pho
togr
aphy
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
SPIN
2.
SPO
T 5
IRS-
P5La
ndsa
t 7 E
TMJE
RS-
1
1-5
year
s0.
25-1
mPa
n-V-
NIR
Aeria
l Pho
togr
aphy
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
Build
ing
heat
loss
E2-b
uild
ing
insu
latio
n su
rvey
s1-
5 ye
ars
1-5
mTI
RAe
rial P
hoto
grap
hy
Met
eoro
logi
cal D
ata
M1-
wea
ther
pre
dict
ion
3-25
min
utes
1-8
kmV-
NIR
-TIR
GO
ESM
ETEO
SAT
M2-
curre
nt te
mpe
ratu
re3-
25 m
inut
es1-
8 km
TIR
GO
ESM
3-cl
ean
air a
nd p
reci
pita
tion
mod
e6-
10 m
inut
es1
kmW
SR-8
8D
Rad
arN
WS
WSR
-88D
M4-
seve
re w
eath
er m
ode
5 m
inut
es1
kmW
SR-8
8D
Rad
arN
WS
WSR
-88D
Pan-
V-N
IR-
Rad
ar-L
iDAR
S2-re
gion
al/n
atio
nal
popu
latio
n es
timat
ion
Ener
gy D
eman
d an
d C
onse
rvat
ion
Pipe
hea
t los
s-(S
team
, hot
wat
er)
E1-e
nerg
y de
man
d an
d pr
oduc
tion
pote
ntia
l
A-6
M5-
mon
itorin
g ur
ban
heat
is
land
effe
ct12
-24
hour
s5-
30 m
TIR
Aeria
l Pho
togr
aphy
Land
/wat
er
boun
dary
IDC
1-st
able
sen
sitiv
e en
viro
nme
1-2
year
s1-
10 m
Pan
-V-N
IR-
MIR
-Rad
ar-
LiD
ARAe
rial P
hoto
grap
hyQ
uick
bird
IKO
NO
S
Cle
ar-c
ut fo
rest
IDO
rbVi
ew 3
/4SP
IN 2
.
SPO
T 5
Fore
st
man
agem
ent
IRS-
P5W
ildlif
e m
anag
emen
tR
ange
land
m
anag
emen
t
Agric
ultu
ral
man
agem
ent
stab
le s
ensi
tive=
wet
land
s,
enda
nger
ed s
peci
es h
abita
ts,
park
s, tr
eatm
ent p
lant
s,
urba
nize
d w
ater
shed
s po
tabl
e w
ater
runo
ff (J
ense
n an
d C
owen
, 199
9:62
0).
Crit
ical
Env
ironm
enta
l Are
a As
sess
men
t
Hea
lth a
nd
abun
danc
e of
Su
spen
ded
sedi
men
t/org
anic
Envi
ronm
enta
l as
sess
men
t (EI
S,
EA)
Hab
itat m
odel
ing
(Bird
stri
ke h
azar
d)Aq
uife
r/wel
l wat
er
reso
urce
Wet
land
/wat
ersh
ed
man
agem
ent
Urb
an v
eget
atio
n m
anag
emen
t
A-7
Qui
ckbi
rd
Orb
View
3/4
DE1
-pre
-em
erge
ncy
imag
ery
1-5
year
s1-
5 m
Aeria
l Pho
togr
aphy
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
SPIN
2.
SPO
T 5
IRS-
P5
Fire
Man
agem
ent
Qui
ckbi
rdD
isas
ter r
espo
nse
IKO
NO
SEn
viro
nmen
tal
clea
nup
Airc
raft
acci
dent
Elev
atio
n da
taO
rbVi
ew 3
/4C
ordo
n to
olR
adar
Dis
aste
r res
pons
eD
E3-d
amag
ed h
ousi
ng s
tock
1-2
days
0.25
-1 m
Pan-
V-N
IR--M
-Li
DAR
Aeria
l Pho
togr
aphy
Cor
don
tool
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
Dis
aste
r res
pons
eD
E4-d
amag
ed tr
ansp
orta
tion
1-2
days
0.25
-1 m
Pan-
V-N
IR--M
-Li
DAR
Aeria
l Pho
togr
aphy
Cor
don
tool
Qui
ckbi
rdIK
ON
OS
Orb
View
3/4
Ther
mal
pol
lutio
nC
2-dy
nam
ic s
ensi
tive
envi
ronm
ents
, whe
re d
ynam
ic
sens
itive
=crit
ical
are
as th
at
coul
d ch
age
rapi
dly
(Jen
sen
and
Cow
en, 1
999:
620)
1-6
mon
ths
0.25
-2 m
Pan-
V-N
IR-
MIR
-TIR
-M
Panc
hrom
atic
onl
y fo
r IKO
NO
S an
d M
ultis
pect
ral o
nly
with
aer
ial t
o m
eet
spat
ial r
es. R
eq.
TIR
, Oth
er
mis
sion
s m
ay
bene
fit fr
om P
an,
V, N
IR, M
IR a
lone
Envi
ronm
enta
l ha
zard
s
Envi
ronm
enta
l as
sess
men
t (EI
S,
EA)
IKO
NO
S
Fire
Res
pons
e G
PS/G
IS T
rack
ing
Syst
emD
E2-p
ost-e
mer
genc
y im
ager
y
Aeria
l Pho
togr
aphy
0.25
-2 m
Aeria
l Pho
togr
aphy
Pan-
V-N
IR-
Rad
ar--M
-Li
DAR
12 h
ours
-2
days
Pan-
V-N
IR--M
-Li
DAR
A-8
Dis
aste
r res
pons
e1-
2 da
ys0.
25-1
mPa
n-V-
NIR
-M-
LiD
ARAe
rial P
hoto
grap
hyC
ordo
n to
olQ
uick
bird
IKO
NO
SFu
ture
Nee
dsN
oise
con
tour
m
appi
ngAs
best
os
iden
tific
atio
n
DE5
-dam
aged
util
ities
, se
rvic
es
B-1
Appendix B: Final Imagery Decision Matrix
This appendix contains the final imagery decision matrix. The matrix has five
columns of data to help decision-makers determine the appropriate imagery for a
particular application. A more detailed discussion of each column is in Chapter 4.
Jensen’s textbook, Jensen and Cowen’s journal article, and a NIMA document
were used as references for the imagery decision matrix and imagery system key. They
are as follows:
Jensen, John R. Remote Sensing of the Environment An Earth Resource Perspective. Upper Saddle River, New Jersey: Prentice-Hall Inc., 2000.
Jensen, John R. and Dave C. Cowen, “Remote Sensing of Urban/Suburban Infrastructure
and Socio-Economic Attributes*,” Photogrammetric Engineering & Remote Sensing, 65: 611-622 (May 1999).
“Status of Selected Current & Future Commercial Remote Sensing Satellites.” National Geospatial Intelligence School, NIMA. Handout from a seminar. 24 June 2002.
B-2
Tabl
e 1.
5. Im
ager
y1.
App
licat
ions
2. T
empo
ral
3. S
patia
l4.
Spe
ctra
lSy
stem
sU
rban
or b
uilt-
up la
nd id
entif
icat
ion
5-10
yea
rs20
-100
mV-
NIR
-MIR
-Rad
ar-M
-LiD
ARC
ateg
ory
1R
esid
entia
l are
as d
elin
eate
d5-
10 y
ears
5-20
mV-
NIR
-MIR
-Rad
ar-M
-LiD
ARC
ateg
ory
2Si
ngle
fam
ily re
side
ntia
l ide
ntifi
ed3-
5 ye
ars
1-5
mPa
n-V-
NIR
-MIR
-M-L
iDAR
Cat
egor
y 3
Faci
lity
elev
atio
ns1-
5 ye
ars
0.25
-0.5
mPa
n-V-
LiD
ARC
ateg
ory
4C
omm
unity
pla
nnin
g1-
5 ye
ars
0.25
-0.5
mPa
n-V-
LiD
ARC
ateg
ory
4Po
pula
tion
estim
atio
n5-
7 ye
ars
0.25
-5 m
Pan-
V-N
IR-R
adar
-LiD
ARC
ateg
ory
4U
rban
spr
awl
5-7
year
s0.
25-5
mPa
n-V-
NIR
-Rad
ar-L
iDAR
Cat
egor
y 4
Build
ing
heat
loss
iden
tifie
d1-
5 ye
ars
1-5
mTI
RC
ateg
ory
3C
omm
erci
al a
nd s
ervi
ces
iden
tifie
d5-
10 y
ears
5-20
mV-
NIR
-MIR
-Rad
ar-M
-LiD
ARC
ateg
ory
2C
omm
erci
al d
elin
eate
d3-
5 ye
ars
1-5
mPa
n-V-
NIR
-MIR
-M-L
iDAR
Cat
egor
y 3
Coa
stal
stru
ctur
e de
sign
5-10
yea
rs0.
25-0
.5 m
Pan-
V-Li
DAR
Cat
egor
y 4
Dra
inag
e pa
ttern
s ob
tain
ed5-
10 y
ears
0.25
-0.5
mPa
n-V-
LiD
ARC
ateg
ory
4Fa
cilit
y m
anag
emen
t1-
5 ye
ars
0.25
-0.5
mPa
n-V-
LiD
ARC
ateg
ory
4As
best
os id
entif
icat
ion
(Fut
ure
appl
icat
ion)
Sing
le C
olle
ctio
n<1
mH
yper
spec
tral
Cat
egor
y 4
Noi
se c
onto
ur m
appi
ng (F
utur
e ap
plic
atio
n)1-
2 ye
ars
1-5
mLi
DAR
Cat
egor
y 3
Pipe
hea
t los
s id
entif
ied-
(Ste
am, h
ot w
ater
)1-
5 ye
ars
1-5
mTI
RC
ateg
ory
3
Tran
spor
tatio
n m
anag
emen
t (re
pairs
)1-
2 ye
ars
0.25
-0.5
mPa
n-V
Cat
egor
y 4
Traf
fic s
tudi
es (p
arki
ng; c
onje
sted
are
as)
5-10
min
utes
0.25
-0.5
mPa
n-V
Cat
egor
y 4
Dig
ging
Per
mits
(site
info
rmat
ion)
1-5
year
s0.
25-0
.5 m
Pan-
V-Li
DAR
Cat
egor
y 4
Hig
hway
alig
nmen
t pla
nnin
g5-
10 y
ears
0.25
-0.5
mPa
n-V-
LiD
ARC
ateg
ory
4U
rban
veg
etat
ion
man
agem
ent
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2
Qua
lity
of li
fe a
naly
sis
(for a
com
mun
ity)
5-10
yea
rs0.
25-3
0 m
Pan-
V-N
IR-R
adar
-LiD
ARC
ateg
ory
2Ag
ricul
ture
iden
tifie
d5-
10 y
ears
20-1
00 m
V-N
IR-M
IR-R
adar
-M-L
iDAR
Cat
egor
y 1
Aqui
fer/w
ell w
ater
reso
urce
man
agem
ent
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2R
ange
land
iden
tifie
d5-
10 y
ears
20-1
00 m
V-N
IR-M
IR-R
adar
-M-L
iDAR
Cat
egor
y 1
Ran
gela
nd m
anag
emen
t1-
2 ye
ars
1-10
mPa
n-V
-NIR
-MIR
-Rad
ar-L
iDAR
Cat
egor
y 2
Envi
ronm
enta
l ass
essm
ent (
EIS,
EA)
site
pl
anni
ng, s
tabl
e en
viro
nmen
t
Agric
ultu
ral m
anag
emen
t (H
ealth
and
abu
ndan
ce
of v
eget
atio
n de
term
ined
)
Min
imum
Res
olut
ion
Req
uire
men
tsIm
ager
y D
ecis
ion
Mat
rix
Tran
spor
tatio
n, A
irfie
ld, C
omm
unic
atio
ns, a
nd
Util
ities
iden
tifie
d
Dis
tanc
e an
d ar
ea m
easu
rem
ents
, bui
ldin
g fo
otpr
ints
1-5
year
s0.
25-0
.5 m
Pan-
V-Li
DAR
5-10
yea
rs5-
20 m
Cat
egor
y 4
V-N
IR-M
IR-R
adar
-M-L
iDAR
Cat
egor
y 2
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2
B-3
Fore
st L
and
iden
tific
atio
n5-
10 y
ears
20-1
00 m
V-N
IR-M
IR-R
adar
-M-L
iDA R
Cat
egor
y 1
Cle
ar-c
ut fo
rest
iden
tific
atio
n1-
2 ye
ars
1-10
mPa
n-V
-NIR
-MIR
-Rad
ar-L
iDAR
Cat
egor
y 2
Wat
er id
entif
icat
ion
5-10
yea
rs20
-100
mV-
NIR
-MIR
-Rad
ar-M
-LiD
A RC
ateg
ory
1La
nd/w
ater
bou
ndar
y id
entif
icat
ion
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2So
il er
osio
n m
odel
ing
5-10
yea
rs0.
25-0
.5 m
Pan-
V-Li
DA R
Cat
egor
y 4
Floo
d pr
even
tion
and
plan
ning
tool
5-10
yea
rs0.
25-0
.5 m
Pan-
V-Li
DA R
Cat
egor
y 4
Hyd
rolo
gy m
odel
ing
5-10
yea
rs0.
25-0
.5 m
Pan-
V-Li
DA R
Cat
egor
y 4
Pollu
tion
iden
tific
atio
n1-
2 ye
ars
1-10
mPa
n-V
-NIR
-MIR
-Rad
ar-L
iDAR
Cat
egor
y 2
Ther
mal
pol
lutio
n of
wat
erw
ays
1-6
mon
ths
0.25
-2 m
Pan-
V-N
IR-M
IR-T
IR-M
Cat
egor
y 3
Wet
land
iden
tific
atio
n5-
10 y
ears
20-1
00 m
V-N
IR-M
IR-R
adar
-M-L
iDA R
Cat
egor
y 1
Hab
itat m
odel
ing
(Bird
stri
ke h
azar
d)1-
2 ye
ars
1-10
mPa
n-V
-NIR
-MIR
-Rad
ar-L
iDAR
Cat
egor
y 2
Wild
life
man
agem
ent
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2M
appi
ng o
f airb
orne
che
mic
al p
lum
es<3
0 m
inut
es0.
25-1
mH
yper
spec
tral
Cat
egor
y 4
Emer
genc
y Ac
tions
Pre-
emer
genc
y im
ager
y1-
5 ye
ars
1-5
mPa
n-V-
NIR
--M-L
iDA R
Cat
egor
y 3
Envi
ronm
enta
l haz
ards
Iden
tific
atio
n1-
6 m
onth
s0.
25-2
mPa
n-V-
NIR
-MIR
-TIR
-MC
ateg
ory
3Fi
re R
espo
nse
GPS
/GIS
Tra
ckin
g Sy
stem
12 h
ours
-2 d
ays
0.25
-2 m
Pan-
V-N
IR-R
adar
--M-L
iDA R
Cat
egor
y 3
Fire
Man
agem
ent
12 h
ours
-2 d
ays
0.25
-2 m
Pan-
V-N
IR-R
adar
--M-L
iDA R
Cat
egor
y 3
Airc
raft
acci
dent
12 h
ours
-2 d
ays
0.25
-2 m
Pan-
V-N
IR-R
adar
--M-L
iDA R
Cat
egor
y 3
Dis
aste
r res
pons
e12
hou
rs-2
day
s0.
25-2
mPa
n-V-
NIR
-Rad
ar--M
-LiD
A RC
ateg
ory
3C
ordo
n to
ol12
hou
rs-2
day
s0.
25-2
mPa
n-V-
NIR
-Rad
ar--M
-LiD
A RC
ateg
ory
3En
viro
nmen
tal c
lean
up12
hou
rs-2
day
s0.
25-2
mPa
n-V-
NIR
-Rad
ar--M
-LiD
A RC
ateg
ory
3El
evat
ion
data
of e
mer
genc
y ar
eas
12 h
ours
-2 d
ays
0.25
-2 m
Pan-
V-N
IR-R
adar
--M-L
iDA R
Cat
egor
y 3
Gen
eral
Top
ogra
phic
Dat
aC
onto
ur g
ener
atio
n5-
10 y
ears
0.25
-0.5
mPa
n-V-
LiD
A RC
ateg
ory
4Sp
ot h
eigh
ts1-
5 ye
ars
0.25
-0.5
mPa
n-V-
LiD
A RC
ateg
ory
43-
dim
ensi
onal
rend
erin
g1-
5 ye
ars
0.25
-0.5
mPa
n-V-
LiD
A RC
ateg
ory
4Ea
rthw
ork
5-10
yea
rs0.
25-0
.5 m
Pan-
V-Li
DA R
Cat
egor
y 4
Mul
tiple
Cat
egor
ies:
The
se a
pplic
atio
ns m
ay
fit m
ultip
le c
ateg
orie
s ab
ove
Fore
st m
anag
emen
t(dec
iduo
us, e
verg
reen
, or
mix
ed id
entif
icat
ion)
Susp
ende
d se
dim
ent/o
rgan
ic m
atte
r id
entif
icat
ion
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2
1-2
year
s1-
10 m
Pan
-V-N
IR-M
IR-R
adar
-LiD
ARC
ateg
ory
2
C-1
Appendix C: Initial Imagery System Key
This appendix contains the initial imagery system key. The matrix has six
columns of data to help decision-makers with detailed capabilities about each imagery
system outlined in the thesis. A more detailed discussion of each column is in Chapter 4.
Jensen’s textbook, Jensen and Cowen’s journal article, and a NIMA document
were used as references for the imagery decision matrix and imagery system key. They
are as follows:
Jensen, John R. Remote Sensing of the Environment An Earth Resource Perspective. Upper Saddle River, New Jersey: Prentice-Hall Inc., 2000.
Jensen, John R. and Dave C. Cowen, “Remote Sensing of Urban/Suburban Infrastructure
and Socio-Economic Attributes*,” Photogrammetric Engineering & Remote Sensing, 65: 611-622 (May 1999).
“Status of Selected Current & Future Commercial Remote Sensing Satellites.” National Geospatial Intelligence School, NIMA. Handout from a seminar. 24 June 2002.
C-2
Tabl
e 2.
SPEC
TRAL
LEG
END
:
Arch
ived
Aer
ial I
mag
ery
Sate
llite
Imag
ery
Qui
ckbi
rd1-
5 da
ys (d
epen
ds o
n la
titud
e)0.
61, 2
.5Pa
n, M
NIM
AM
inIK
ON
OS
2-3
days
1, 4
Pan,
MN
IMA
Min
Orb
View
-3,4
< 3
days
1, 4
Pan,
MN
IMA
Min
SPIN
-2.
45 d
ays
2, 1
0Pa
n(pa
nora
mic
), Pa
nN
IMA
Min
SPO
T-1,
2,3,
426
day
s (1
day
with
3 s
atel
lites
)10
, 20
Pan,
MN
IMA
Min
SPO
T 5
< 3
days
(1 d
ay w
ith 3
sat
ellit
es)
2.5,
5, 1
0Pa
n, P
an, M
,
NIM
A po
ssib
le, S
POT
Imag
e (h
ttp://
ww
w.s
pot.c
om)
Min
Land
sat-1
,2,3
MSS
18 d
ays
56x7
9M
NIM
AM
in
Imag
ery
Syst
em K
ey-In
itial
MIR
=Mid
-infra
red
incl
udin
g bl
ack
and
whi
te in
frare
d, c
olor
infra
red(
1.5-
2.5µ
m)
TIR
=The
rmal
infra
red(
3-12
µm)
Rad
ar=A
eria
l Rad
arN
ote:
Item
s in
ital
ics
are
abov
e th
e m
inim
um s
pect
ral r
esol
utio
n re
quire
men
t, ho
wev
er, t
he im
ager
y sy
stem
cho
sen
mus
t als
o m
eet t
he m
inim
um te
mpo
ral a
nd s
patia
l res
olut
ion.
Whe
re M
ultis
pect
ral i
s no
t use
d in
the
tabl
e, it
is n
ot n
eede
d fo
r the
requ
irem
ent.
WSR
-88D
Rad
ar=g
roun
d-ba
sed
Nat
iona
l Wea
ther
Ser
vice
Wea
ther
Sur
veilla
nce
Rad
ar
Not
e: L
iDAR
is u
sed
mai
nly
for e
leva
tion
data
; an
addi
tiona
l im
ager
y sy
stem
suc
h as
Pan
is u
sed
to
mee
t the
requ
irem
ents
of t
he m
issi
on
L=Li
ght D
etec
tion
and
Ran
ging
(LiD
AR)
Pan,
M, T
IRM
in
Pan,
V, N
IR, M
IR, T
IR, M
USG
S, C
omm
erci
alM
in
Land
sat-4
,5 M
SS a
nd T
M16
day
s30
, 56x
79,
120
NIM
AM
in
6.
Cos
t(Qua
litat
ive)
1. Im
ager
y Sy
stem
2. T
empo
ral R
esol
utio
n
3. S
patia
l R
esol
utio
n (m
)4.
Spe
ctra
l Res
olut
ion
5. A
vaila
bilit
y
M=M
ulti-
spec
tral (
Incl
udes
V, N
IR,M
IR, a
nd d
epen
ding
on
sens
or; T
IR)
Pan=
Blac
k an
d w
hite
pan
chro
mat
ic(0
.5-0
.7µm
)V=
visi
ble
colo
r(0.4
-0.7
µm)
NIR
=Nea
r-inf
rare
d in
clud
ing
blac
k an
d w
hite
infra
red,
col
or in
frare
d (0
.7-1
.1µm
)
M, M
, TIR
(has
two
mul
tispe
ctra
l sca
nner
s,
Them
atic
Map
per a
nd M
SS)
NIM
A po
ssib
le, U
SGS
(http
://la
ndsa
t7.u
sgs.
gov/
inde
x.ph
p)
Varie
s de
pend
ing
airc
raft
altit
ude
Aeria
l Im
ager
y, P
an o
r V12
hou
rs
Land
sat-7
ETM
+16
day
s15
, 30,
60
C-3
Rad
ar
Min
ATLA
S(ae
rial)
On
Dem
and
wea
ther
per
mitt
ing
2.5
at 6
000
ft AG
LM
incl
udin
g TI
R b
and
Dae
dalu
s(ae
rial)
On
Dem
and
wea
ther
per
mitt
ing
2.5
at 1
000
m A
GL
M in
clud
ing
TIR
ban
d
ASTE
R4
to 1
6 da
ys15
, 30,
90
V, N
IR, M
IR; N
IR, M
IR; T
IR
Hig
h
IRS-
P5Se
arch
ing
for t
empo
ral
2.5
Pan
Hig
h
IRS-
1 C
D24
day
s5.
8, 7
0, 1
88,
23.5
Pa
n, M
IR, (
R a
nd N
IR),
M
IRS-
1 AB
22 d
ays
(11
days
with
bot
h sa
tellit
es)
36.2
5, 7
2.5
M, M
Hig
h
SIR
-A2.
5 da
ys o
nly
in 1
978
40x4
0L-
band
Rad
ar
SIR
-B8
days
onl
y in
198
417
x25
L-ba
nd R
adar
Min
NIM
A po
ssib
le
(http
://w
ww
.spa
tial.m
ain
e.ed
u/~t
ony/
ICIP
01Pe
te.P
DF)
, Com
mer
cial
co
mpa
nies
NAS
A St
enni
s Sp
ace
Cen
ter
(http
://w
ww
ghcc
.msf
c.n
asa.
gov/
prec
isio
nag/
atl
asre
mot
e.ht
ml)
NAS
A Ea
rth O
bser
ving
Sy
stem
(EO
S) G
atew
ay
(http
://ed
cim
sww
w.c
r.us
gs.g
ov/p
ub/im
swel
com
e/)
Min
Hig
h
Min
Spac
e Im
agin
g, In
c (h
ttp://
ww
w.s
pace
imag
ing
me.
com
/)Sp
ace
Imag
ing,
Inc
(http
://w
ww
.spa
ceim
agi
ngm
e.co
m/)
Spac
e Im
agin
g, In
c (h
ttp://
ww
w.s
pace
imag
ing
me.
com
/)
NAS
A Ea
rth O
bser
ving
Sy
stem
(EO
S) G
atew
ay
(http
://ed
cim
sww
w.c
r.us
gs.g
ov/p
ub/im
swel
com
e/) N
ASA
Earth
Obs
ervi
ng
Syst
em (E
OS)
Gat
eway
(h
ttp://
edci
msw
ww
.cr.u
sgs
.gov
/pub
/imsw
elco
me
/)
Mul
ti-sp
ectr
al A
eria
l Im
ager
y
C-4
JER
S-1
From
199
2 to
199
818
x24,
18
x18
M, L
-ban
d R
adar
NIM
AM
in
RAD
ARSA
T-1
From
199
5 to
pre
sent
, 24
days
re
visi
t10
-100
x10-
100
C-b
and
Rad
arN
IMA
(RAD
ARSA
T-1)
Min
ERS-
1,2.
From
199
1/19
95 to
pre
sent
30x2
6C
-ban
d R
adar
NIM
AM
in
LiD
AR
SPO
T-4
Veg
1O
rbVi
ew-2
Sea
WiF
S1.
13N
IMA
Min
GO
ES15
min
utes
U.S
. (Je
nsen
, 20
00:2
03)
1, 4
, 8VI
S, T
IR, T
IRM
ETEO
SAT
NW
S W
SR-8
8D
Hig
h
3. S
patia
l R
esol
utio
n(km
)4.
Spe
ctra
l Res
olut
ion
AVH
RR
24 h
ours
(Jen
sen,
200
0:20
5)1.
1, 4
L (L
aser
)Pr
ivat
e
MO
DIS
5. A
vaila
bilit
y
6.
Cos
t(Qua
litat
ive)
LiD
AR12
hou
rs
1. Im
ager
y Sy
stem
2. R
evis
it Te
mpo
ral R
esol
utio
n
15 x
15-
30S-
band
Rad
arM
ediu
m
STAR
3i
3x3
X-ba
nd R
adar
Min
Not
cle
ar w
here
to
obta
in
(http
://w
ww
.hob
bysp
ace
.com
/Sat
Eyes
/#Sp
ysat
Im
ages
)
NIM
A, In
term
ap
(http
://w
ww
.inte
rmap
tec
hnol
ogie
s.co
m/in
term
ap_h
ome_
page
.htm
)
10 d
ays
only
in 1
994
SIR
-C
Alm
az-1
From
199
1 fo
r 18
mon
ths
Min
X, C
, and
L-b
and
Rad
ar10
-30x
30
.250
to 1
(J
ense
n,
M, M
with
resa
mpl
ing
for
high
er fr
eque
ncy
NAS
A Ea
rth O
bser
ving
Sy
stem
(EO
S) G
atew
ay
(http
://ed
cim
sww
w.c
r.us
gs.g
ov/p
ub/im
swel
com
e/)
Sate
llite
Imag
ery-
Low
R
esol
utio
n
Pres
ently
in o
pera
tion
0.2
horiz
onta
l, 0.
1 ve
rtica
l
D-1
Appendix D: Final Imagery System Key
This appendix contains the final imagery system key. The matrix has eight
columns of data to help decision-makers with detailed capabilities about each imagery
system outlined in the thesis. A more detailed discussion of each column is in Chapter 4.
Jensen’s textbook, Jensen and Cowen’s journal article, and a NIMA document
were used as references for the imagery decision matrix and imagery system key. They
are as follows:
Jensen, John R. Remote Sensing of the Environment An Earth Resource Perspective. Upper Saddle River, New Jersey: Prentice-Hall Inc., 2000.
Jensen, John R. and Dave C. Cowen, “Remote Sensing of Urban/Suburban Infrastructure
and Socio-Economic Attributes*,” Photogrammetric Engineering & Remote Sensing, 65: 611-622 (May 1999).
“Status of Selected Current & Future Commercial Remote Sensing Satellites.” National Geospatial Intelligence School, NIMA. Handout from a seminar. 24 June 2002.
D-2
Aeria
l Im
ager
y
Hig
hC
omm
erci
al
Med
Low
, H
igh
M (8
ban
ds fr
om
UV
to N
IR, a
nd 2
TI
R b
ands
)
M (6
ban
ds in
V
and
NIR
, 2 M
IR,
6 TI
R)
Tabl
e 2.
SPEC
TRAL
LEG
END
:
1.
Imag
ery
Cat
egor
y -R
ange
1
to 4
(low
to
hig
h)
NIR
=Nea
r-in
frare
d in
clud
ing
blac
k an
d w
hite
infra
red,
col
or in
frare
d (0
.7-1
.1µm
)M
IR=M
id-in
frare
d in
clud
ing
blac
k an
d w
hite
infra
red,
col
or in
frare
d(1.
5-2.
5µm
)
2. Im
ager
y Sy
stem
3. T
empo
ral
Res
olut
ion
4. S
patia
l R
esol
utio
n (m
)5.
Spe
ctra
l R
esol
utio
n6.
Ava
ilabi
lity
7.
Cos
t(Q
ualit
ativ
e)8.
Gen
eral
Use
Prec
isio
n m
easu
rem
ents
an
d an
alys
is
Prec
isio
n m
easu
rem
ents
an
d an
alys
is
Prec
isio
n m
easu
rem
ents
an
d an
alys
isPr
ecis
ion
mea
sure
men
ts
and
anal
ysis
2.5
at 1
000
m
AGL
Dep
ends
on
airc
raft
altit
ude
0.2
horiz
onta
l, 0.
1 ve
rtica
l
2.5
at 6
000
ft AG
L
On
Dem
and
wea
ther
pe
rmitt
ing
On
Dem
and
wea
ther
pe
rmitt
ing
On
Dem
and
wea
ther
pe
rmitt
ing
On
Dem
and
wea
ther
Low
, H
igh
Not
e: It
ems
in it
alic
s ar
e ab
ove
the
min
imum
spe
ctra
l res
olut
ion
requ
irem
ent,
how
ever
, the
imag
ery
syst
em c
hose
n m
ust a
lso
mee
t the
min
imum
tem
pora
l and
sp
atia
l res
olut
ion.
Whe
re M
ultis
pect
ral i
s no
t use
d in
the
tabl
e, it
is n
ot n
eede
d fo
r th
e re
quire
men
Pan=
Blac
k an
d w
hite
pan
chro
mat
ic(0
.5-0
.7µm
)V=
visi
ble
colo
r(0.4
-0.7
µm)
L=Li
ght D
etec
tion
and
Ran
ging
(LiD
AR)
Rad
ar=A
eria
l or s
atel
lite
Rad
ar
M=M
ulti-
spec
tral (
Incl
udes
V, N
IR,M
IR, a
nd d
epen
ding
on
sens
or; T
IR)
Not
e: L
iDAR
is u
sed
mai
nly
for e
leva
tion
data
; an
addi
tiona
l im
ager
y sy
stem
suc
h as
Pan
is u
sed
to m
eet t
he re
quire
men
ts o
f the
mis
sion
B=Bl
ue (0
.4-0
.5µm
)
TIR
=The
rmal
infra
red(
3-12
µm)
G=G
reen
(0.5
-0.6
µm)
R=R
ed (0
.6-0
.7µm
)
Imag
ery
Syst
em K
ey
L (L
aser
)
Dae
dalu
s(ae
rial)
ATLA
S(ae
rial)
4Li
DAR
3 or
4
depe
nds
on a
ltitu
de
3 or
4
depe
nds
on a
ltitu
de
Aeria
l Im
ager
y
NIM
A po
ssib
le
(http
://w
ww
.spa
tial.m
aine
.edu
)
NAS
A St
enni
s Sp
ace
Cen
ter(h
ttp://
ww
wgh
cc.
msf
c.na
sa.g
ov)
4Pa
n, V
, NIR
, M
IR, T
IR, M
USG
S, C
omm
erci
al
D-3
Sate
llite
Imag
ery
3O
rbVi
ew-3
,4<
3 da
ys4
M (B
, G, R
, NIR
)N
IMA
Min
Loca
l mul
tispe
ctra
l an
alys
is
2SP
IN-2
.45
day
s10
Pan
NIM
AM
inLo
cal a
naly
sis
3SP
OT
5<
3 da
ys (1
day
w
ith 3
sat
ellit
es)
2.5
Pan
NIM
A po
ssib
le, S
POT
Imag
e (h
ttp://
ww
w.s
pot.c
om)
Min
, H
igh
Loca
l ana
lysi
s
1La
ndsa
t-1,2
,3 M
SS18
day
s24
0TI
RN
IMA
Min
Re g
iona
l the
rmal
ana
lysi
s
1La
ndsa
t-4,5
TM
16 d
ays
120
TIR
NIM
AM
inR
egio
nal t
herm
al a
naly
sis
Min
Min
30
NIM
A
56x7
9
52010
Min
Min
NIM
A
Pan
M (G
, R, N
IR,
MIR
)
NIM
APa
n
NIM
A
NIM
A
26 d
ays
(1 d
ay
with
3 s
atel
lites
)
Prec
isio
n m
easu
rem
ents
an
d an
alys
isLo
cal m
ultis
pect
ral
anal
ysis
1-5
days
(d
epen
ds o
n 1-
5 da
ys
(dep
ends
on
2.5
Qui
ckbi
rd
16 d
ays
Land
sat-4
,5 M
SS
Land
sat-4
,5 T
M
SPO
T-4
SPO
T-4
SPO
T 5
Land
sat-1
,2,3
MSS
26 d
ays
(1 d
ay
with
3 s
atel
lites
)
< 3
days
(1 d
ay
with
3 s
atel
lites
)
16 d
ays
18 d
ays
56x7
9
26 d
ays
(1 d
ay
with
3 s
atel
lites
)
Prec
isio
n m
easu
rem
ents
an
d an
alys
is
Reg
iona
l mul
tispe
ctra
l an
alys
is
Reg
iona
l mul
tispe
ctra
l an
alys
is
Min
Min
Prec
isio
n m
easu
rem
ents
an
d an
alys
isLo
cal m
ultis
pect
ral
anal
ysis
Prec
isio
n m
easu
rem
ents
an
d an
alys
is
Min
Loca
l ana
lysi
s
NIM
AM
(B, G
, R, N
IR)
Min
Pan(
pano
ram
ic)
NIM
A
NIM
A
4Q
uick
bird
0.61
Pan
3
Loca
l mul
tispe
ctra
l an
alys
isM
(G, R
, NIR
, N
IR)
Pan
Pan
NIM
A
M (G
, R, N
IR,
NIR
)M
(B, G
, R, N
IR,
MIR
, MIR
)
M (B
, G, R
, NIR
)
NIM
A
NIM
A
NIM
A
4IK
ON
OS
2-3
days
1
3IK
ON
OS
2-3
days
4
4O
rbVi
ew-3
,4<
3 da
ys1
3SP
IN-2
.45
day
s2
2SP
OT-
1,2,
310
Pan
2SP
OT-
1,2,
320
M (G
, R, N
IR)
26 d
ays
(1 d
ay
with
3 s
atel
lites
)
2 3 1 112M
in
NIM
AM
in
Loca
l ana
lysi
s
Min
Loca
l ana
lysi
s
2SP
OT
5<
3 da
ys (1
day
w
ith 3
sat
ellit
es)
10N
IMA
Min
M (G
, R, N
IR,
MIR
)
Min
NIM
AM
in
Min
Reg
iona
l mul
tispe
ctra
l an
alys
is
Reg
iona
l mul
tispe
ctra
l an
alys
isR
egio
nal m
ultis
pect
ral
anal
ysis
D-4
2La
ndsa
t-7 E
TM+
16 d
ays
15Pa
nLo
cal a
naly
sis
1La
ndsa
t-7 E
TM+
16 d
ays
60TI
RR
egio
nal t
herm
al a
naly
sis
3IR
S-1
CD
24 d
ays
5.8
Pan
Loca
l ana
lysi
s
3IR
S-P5
To b
e de
term
ined
2.5
Pan
Spac
e Im
agin
g, In
c (h
ttp://
ww
w.s
pace
imag
ing
me.
com
/)H
igh
Loca
l ana
lysi
s
1AS
TER
4 to
16
days
905
TIR
ban
dsR
egio
nal t
herm
al a
naly
sis
2JE
RS-
1Fr
om 1
992
to
1998
18M
(G, R
, 2 N
IR, 4
M
IR)
NIM
AM
inR
egio
nal a
naly
sis
Rad
ar
Min
NIM
A po
ssib
le, U
SGS
(http
://la
ndsa
t7.u
sgs.
gov/
inde
x.ph
p)M
in,
Min
Spac
e Im
agin
g, In
c (h
ttp://
ww
w.s
pace
imag
ing
me.
com
/)H
igh
NAS
A Ea
rth O
bser
ving
Sy
stem
(EO
S)
Gat
eway
(http
://ed
cim
sw
ww
.cr.u
sgs.
gov)
M (G
, R, N
IR,
NIR
)
R, N
IR
M (G
, R, N
IR)
Min
Min
NAS
A Ea
rth O
bser
ving
Sy
stem
(EO
S)
Gat
eway
(http
://ed
cim
sw
ww
.cr.u
sgs.
gov)
NAS
A Ea
rth O
bser
ving
Sy
stem
(EO
S)
Gat
eway
(http
://ed
cim
sw
ww
.cr.u
sgs.
gov)
Land
sat-7
ETM
+16
day
s
24 d
ays
24 d
ays
Reg
iona
l mul
tispe
ctra
l an
alys
is
Loca
l mul
tispe
ctra
l an
alys
is
Reg
iona
l mul
tispe
ctra
l an
alys
is
Reg
iona
l mul
tispe
ctra
l an
alys
is
Reg
iona
l mul
tispe
ctra
l an
alys
is
Reg
iona
l the
rmal
ana
lysi
s
2.5
days
onl
y in
19
78
8 da
ys o
nly
in
1984
6 N
IR to
MIR
ba
nds
2 21 1 1
M (B
, G, R
, NIR
, M
IR, M
IR)
Spac
e Im
agin
g, In
c (h
ttp://
ww
w.s
pace
imag
ing
me.
com
/)H
igh
M (B
, G, R
, NIR
)
30
1IR
S-1
AB36
.25
22 d
ays
(11
days
w
ith b
oth
sate
llites
)
Reg
iona
l mul
tispe
ctra
l an
alys
is
1IR
S-1
AB72
.5M
(B, G
, R, N
IR)
Reg
iona
l mul
tispe
ctra
l an
alys
is22
day
s (1
1 da
ys
with
bot
h sa
tellit
es)
IRS-
1 C
D24
day
s70
MIR
IRS-
1 C
D
IRS-
1 C
D23
.5
188
ASTE
R4
to 1
6 da
ys15
1AS
TER
4 to
16
days
30
Reg
iona
l ana
lysi
s
1SI
R-B
17 x
25
L-ba
nd R
adar
Reg
iona
l ana
lysi
s
1SI
R-A
40 x
40
L-ba
nd R
adar
D-5
2JE
RS-
1Fr
om 1
992
to
1998
18L-
band
Rad
arN
IMA
Min
Reg
iona
l ana
lysi
s
2Al
maz
-1Fr
om 1
991
for 1
8 m
onth
s15
x15-
30S-
band
Rad
ar
(http
://w
ww
.hob
bysp
ace.
com
/Sat
Eyes
/#Sp
ysa
tImag
es)
Med
Reg
iona
l ana
lysi
s
3x3
X-ba
nd R
adar
Min
Loca
l ana
lysi
sN
IMA,
Inte
rmap
(h
ttp://
ww
w.in
term
apte
chno
logi
es.c
om/in
term
ap_h
ome_
page
.htm
)
Min
NAS
A Ea
rth O
bser
ving
Sy
stem
(EO
S)
Gat
eway
(h
ttp://
edci
msw
ww
.cr.u
sgs.
gov)
NIM
A
From
199
5 to
pr
esen
t, 24
day
s Fr
om 1
991/
1995
to
pre
sent
Pres
ently
in
oper
atio
n
10 d
ays
only
in
1994
10-3
0 x
30X,
C, a
nd L
-ban
d R
adar
1SI
R-C
Reg
iona
l ana
lysi
s
1R
ADAR
SAT-
110
-100
x10-
100
C-b
and
Rad
arN
IMA
(RAD
ARSA
T-1)
Min
Reg
iona
l ana
lysi
s
Reg
iona
l ana
lysi
s
STAR
3i
1ER
S-1,
2.30
x26
C-b
and
Rad
ar
3
Min
E-1
Appendix E: Instruction Sheet
Instruction Sheet for Using the Imagery Decision Matrix and the Imagery System Key
Table 1-Imagery Decision Matrix
The Imagery Decision Matrix is used to determine what type(s) of remote sensing
imagery are applicable for a specific Air Force requirement. The electromagnetic
spectrum used in remote sensing consists of wavelengths from ultraviolet light to
microwaves (Radar). Remote sensing makes use of these wavelengths to produce
images.
The following columns are used in the matrix:
1. Applications Air Force applications that were identified through a review of
current literature, an Air Force Geo-Integration Office (AF/GIO) survey of 85 CONUS
bases, and a 2002 Geospatial Technologies Symposium & Exposition. The matrix can be
used to determine the imagery that best suits an application or as an educational tool to
learn about the imagery types and capabilities that are available. Many potential
applications are identified in Column 1 of the matrix. Applications are organized
according to classification areas such as Urban or built-up land, Agriculture, Rangeland,
Forest Land, Water, Wetland, and Multiple.
2. Temporal Resolution Identifies the amount of time that imagery data can be
used before a refresh of imagery is suggested. This takes into account the timeliness of
the imagery for a given application. For instance, if you want distance and area
measurements, the imagery should be updated every one to five years at a minimum.
E-2
3. Spatial Resolution The minimum spatial resolution that is necessary for an
analyst to see a particular level of detail and distinguish earth features of interest for a
particular application. For instance, a 1 x 1 meter spatial resolution image will allow an
analyst to determine information about features that are 2 x 2 meter in size or larger.
Each 1 x 1 meter square is called a pixel. A minimum of two pixels wide in the x-
direction and two pixels wide in the y-direction (or 4 pixels) is required to determine
information about features of interest.
y-axis 2-meter
x-axis
Figure E-1: Spatial Resolution of Features
4. Spectral Resolution Spectral resolution allows an analyst to determine
information from an image based on spectral reflectance of features. Various parts of the
electromagnetic spectrum that an imagery system may ‘see’ include ultraviolet; visible
green, blue, and red; near infrared; mid-infrared; thermal infrared; radar; and laser. The
spectral resolution capability of each remote sensing system varies. For example, the
sensor onboard the Quickbird satellite can ‘see’ ultraviolet; visible green, blue, and red;
near-infrared, and mid-infrared wavelengths of the electromagnetic spectrum. Quickbird
uses these wavelengths to produce imagery.
1x1 pixel
E-3
5. Imagery Systems Each application receives a category code corresponding to
the imagery that will be useful. Imagery systems are identified further in Table 2, the
imagery system key, with appropriate category code, capabilities, availability, cost, and
general use.
Table 2-Imagery System Key
The imagery system key provides further information about the imagery systems
that fulfill requirements of each application. At the top of the key, the spectral legend
identifies the meaning of acronyms used in the imagery decision matrix and imagery
system key. The wavelengths of each section of the electromagnetic spectrum are
identified. For instance, Pan=Black and white panchromatic (0.5-0.7 µm). This means
that black and white panchromatic collects information on the electromagnetic spectrum
using 0.5-0.7 µm wavelengths. Characteristics of each imagery system are listed as
opposed to the minimum imagery requirements identified in the matrix. Columns
included are imagery category, imagery system, temporal resolution, spatial resolution,
spectral resolution, availability of imagery, qualitative cost, and general uses. A
description of each follows:
1. Imagery Category- Range 1 to 4 (low to high) Capability Each imagery
system is categorized according to the capability of the imagery. The rank is based on
spatial resolution. A category 1 imagery system provides the least spatial resolution,
while a category 4 imagery system has the best spatial resolution.
2. Imagery System The name of each imagery system is identified.
E-4
3. Temporal Resolution (hours or days) Identifies the frequency of obtaining
imagery covering a specific geographic area, weather permitting. With the exception of
Radar and LiDAR, all of the imagery systems require non-cloudy conditions to obtain
imagery. LiDAR may be collected in cloudy conditions only if the aerial platform
(airplane or helicopter) can fly beneath the cloud-covered area. For satellite imagery, a
satellite is on a predetermined orbiting path and imagery is available at a set time such as
every two days at best for IKONOS. The temporal resolution is the highest frequency
possible if a user has requested imagery of the same geographic area each time the
satellite orbits overhead. A satellite has a wide field of view, however only a portion of
the geographic area is imaged depending on the spatial resolution of the satellite and user
requests. For aerial imagery, the amount of time required to obtain the first set of
imagery on demand will vary. Once the first fly-over is coordinated, the frequency of
obtaining imagery should be a relatively short period of time such as 12 hours or less.
4. Spatial Resolution (meters) The spatial resolution is the level of detail an
imagery system can record. For instance, IKONOS 1-meter spatial resolution is higher
than IKONOS 4-meter spatial resolution. The 1-meter IKONOS will have 16 pixels
covering a 4x4 meter area. The 4-meter IKONOS will have 1 pixel covering the same
4x4 meter area.
E-5
IKONOS 1-Meter Panchromatic IKONOS 4-Meter Multi-Spectral
4 meters 4 meters
Figure E-2: Spatial Resolution of Imagery System
5. Spectral Resolution (spectral bands) The spectral resolution of an imagery
system may provide imagery that uses one or more of the ultraviolet, red, green, blue,
near-infrared, mid-infrared, thermal infrared, and radar wavelengths. Most imagery
systems have more than one subsystem that can record electromagnetic energy to produce
an image. For example, Quickbird has two imaging subsystems. The imagery available
from Quickbird is 0.61-meter panchromatic imagery and 2.5 meter multi-spectral
imagery.
6. Availability Suggested sources on where to find the imagery through
government or commercial sources. Each source should be investigated to determine the
extent of imagery products available. Issues of formatting and availability for a particular
area have to be determined on a case-by-case basis.
7. Cost (Qualitative) The qualitative cost of imagery. Minimal costs take into
account fees associated with mailing and miscellaneous fees. Low cost refers to less than
E-6
$2000 per image, while medium and high costs refer to $2000 to $5000, and above $5000
respectively. The cost of imagery will vary depending on the format. Formats, for
example, are digital, hard copy, or downloaded through an Internet site.
8. General Use Imagery systems are grouped according to researcher-defined
geographic area coverage: precision, local, or regional. These are qualitative measures
that indicate how the imagery system can be used. Precision denotes the highest spatial
resolution of <2 meters and would allow decisions to be made about a single base facility.
Local represents a resolution of 2-15 meters, displaying a broader view of an Air Force
base. Regional, with the least spatial resolution capability, >15 meters, would encompass
an area as large as a county or state. Multi-spectral and thermal analysis is pointed out
for imagery systems with this capability.
How to use the Imagery Decision Matrix and Imagery System Key:
1. Starting with the imagery decision matrix, identify what your required
application is such as distance and area measurements, habitat modeling, and
facility elevations in Column 1.
2. The minimum temporal, spatial, and spectral resolution is given in Columns 2
through 4. (See the spectral legend in the imagery system key for explanation
of spectral acronyms). If your application is not represented, some
interpolation may be made to determine imagery useful for your application.
3. The imagery system, Column 5, will identify the appropriate category code
required to meet the needs of each specific application. Categories identified
here are the same categories identified in Column 1 of Table 2. Each user will
E-7
need to determine the best imagery system for each application. Note that
imagery categories range from 1 to 4, lowest to highest spatial resolution.
4. Table 2, the imagery system key, has information on the category code,
temporal resolution, spatial resolution, spectral resolution, availability,
qualitative cost, and general use designation for each imagery system.
Example:
1. You have been tasked to determine buildings on base that may have a problem
with excessive heat loss. Your base is also constructing 200 new housing
units and would like to make sure that all of them have appropriate insulation
upon completion. The units will be constructed over a 2-year time period in
phases of 25 units every three months.
2. In Column 1 of the decision matrix, the applications column, you will find
building heat loss identification represented.
3. Note the corresponding information about your application in Column 2
(temporal resolution), Column 3 (spatial resolution), and Column 4 (spectral
resolution). The data in these columns is 1-5 years, 1-5 meters, and TIR
respectively. (See the spectral legend in the imagery system key for
explanation of spectral acronyms). Depending on your specific project, you
may have stricter requirements for each of the resolutions. For your project,
you are constructing 200 new family housing units in 8 phases beginning
every 3 months. Your dynamic situation requires a higher temporal
resolution. You may want to obtain imagery every three months instead of
every one to five years.
E-8
4. If your resolution requirements are met, Column 5 (imagery systems) will
identify the appropriate imagery category for building heat loss. Category 3 is
designated for this application.
5. Refer to Table 2, Column 1 of the imagery system key, and determine an
imagery system of category 3 or higher with thermal infrared capability.
(Remember that the category codes account only for the spatial resolution.)
The imagery systems that will meet your requirements are Daedalus (aerial
and ATLAS (aerial). You can determine the one that best meets your needs
based on cost and availability. Suppose you choose ATLAS. Your next goal
will be to obtain on-demand thermal-infrared aerial imagery every three
months. However, due to the expense of obtaining on-demand imagery, you
may want to wait until the project is completed (instead of requesting imagery
for every three-month construction phase) to request a thermal infrared
imagery fly-over. Additional information on the temporal resolution, spatial
resolution, spectral resolution, availability, qualitative cost, and general use
are also provided. Note that as imagery becomes more widely used, and more
competition develops, the cost of imagery may be reduced.
F-1
Appendix F: Imagery Decision Flowchart
Budg
etFr
eque
ncy
Tim
elin
eSp
atia
l Res
olut
ion
Spec
tral R
esol
utio
n
>20m
Yes
No
Yes
No
MS
no T
IRM
S w
/ TIR
Pan
Yes
Yes
No
No
Land
sat 7
(Pan
)La
ndsa
t 7(M
S)La
ndsa
t 7(T
IR)
Nex
t pag
e
Elev
atio
ns
Yes Li
DAR
No
Clo
udco
vere
dYe
s
RAD
ARSA
T-1
Cat
egor
y 1
Exte
rnal
Con
side
ratio
ns
F-2
Spat
ial R
esol
utio
n
Spec
tral R
esol
utio
n
5-20
m
Yes
No
Yes
No
MS
no T
IRM
S w
/ TIR
Pan
Yes
Yes
No
No
Land
sat 7
(Pan
)SP
OT
5(M
S)AT
LAS
(TIR
)
Nex
t pag
e
Elev
atio
ns
Yes Li
DAR
No
Clo
udco
vere
dYe
s JER
S-1
Cat
egor
y 2
Budg
etFr
eque
ncy
Tim
elin
e
Exte
rnal
Con
side
ratio
ns
F-3
Spat
ial R
esol
utio
n
Spec
tral R
esol
utio
n
1-5m
Yes
No
Yes
No
MS
no T
IRM
S w
/ TIR
Pan
Yes
Yes
No
No
SPO
T 5
(Pan
)Q
uick
bird
(MS)
ATLA
S(T
IR)
Nex
t pag
e
Elev
atio
ns
Yes Li
DAR
No
Clo
udco
vere
dYe
s STAR
-3i
Cate
gory
3
Budg
etFr
eque
ncy
Tim
elin
e
Exte
rnal
Con
side
ratio
ns
F-4
Spat
ial R
esol
utio
n
Spec
tral R
esol
utio
n
<1m
Yes
No
Yes
No
MS
no T
IRM
S w
/ TIR
Pan
Yes
Yes
No
No
Qui
ckbi
rd(P
an)
Qui
ckbi
rd(M
S)Fu
ture
Gen
ATLA
S(T
IR)
Low
leve
lfli
ght
Nex
t pag
e
Elev
atio
ns
Yes Li
DAR
No
Clo
udco
vere
dYe
s STAR
-3i
Futu
re G
en
Cat
egor
y 4
Budg
etFr
eque
ncy
Tim
elin
e
Exte
rnal
Con
side
ratio
ns
G-1
List of Symbols, Abbreviations and Acronyms
AAA Anti-Aircraft Artillery AFB Air Force Base AF/GIO Air Force Geo-Integration Office AFMC Air Force Materiel Command AGL Above Ground Level Almaz-1 Russian SAR Imagery, meaning diamond ASC Aeronautical Systems Center ASTER Advanced Spaceborne Thermal Emission and Reflection
Radiometer. Multi-spectral sensor on the NASA TERRA satellite. ATLAS Advanced Thermal and Land Applications Sensor. Also referred
to as Airborne Terrestrial Applications Sensor. NASA Multi-spectral airborne sensor.
AVHRR Advanced Very High Resolution Radiometer BAM Bird Avoidance Model CAD Computer-Aided Drawing CIA Central Intelligence Agency CIP Common Installation Picture CONUS Continental United States CSIL Commercial Satellite Imagery Library DIA Defense Intelligence Agency DoD Department of Defense DoDD Department of Defense Directive DOE Department of Energy DOQ Digital Orthophoto Quadrangle DRU Direct Reporting Unit DVD-R Digital Versatile Disc Recordable EOS Earth Observing System EPA Environmental Protection Agency EROS Earth Resources Observation Systems Data Center ERS-1, 2 European Space Agency Radar ESRI Environmental Systems Research Institute, Inc. FBI Federal Bureau of Investigation FTP File Transfer Protocol GB Gigabytes-Equal to 1,024 Megabytes GHz Gig Hertz, Computer processing speed GIO Geo-Integration Office/Officer GIS Geographic Information System GOES Geostationary Operational Environmental Satellite GWAC Government Wide Agency Contract ID/IQ Indefinite Delivery/Indefinite Quantity IKONOS Greek word for image. American Remote Sensing Satellite IRS Indian Remote Sensing Satellite
G-2
IT Information Technology JERS-1 Japanese Environmental Remote Sensing Radar Landsat ETM+ Land Satellite Enhanced Thematic Mapper Plus Landsat MSS Land Satellite Multi-spectral Scanner Landsat TM Land Satellite Thematic Mapper LiDAR Light Detection and Ranging M Multi-spectral MAJCOM Major Command METEOSAT Meteorological Satellite Min Minimum MIR Mid-Infrared MODIS Moderate Resolution Imaging Spectroradiometer MOS Minimum Operating Strip MSS Multi-spectral Scanner NAIC National Air Intelligence Center NASA National Aeronautics and Space Administration NIMA National Imagery and Mapping Agency NIR Near-Infrared NOAA National Oceanic and Atmospheric Administration NRO National Reconnaissance Office NSA National Security Agency NWS WSR-88D National Weather Service, Weather Surveillance Radar 88 Doppler OrbView Multi-spectral Satellite Named derived from the ORBIMAGE
company. PACAF Pacific Air Forces Pan Panchromatic RADAR Radio Detection and Ranging RAM Random-Access Memory RRR Rapid Runway Repair SAIC Science Applications International Corporation SAM Surface-To-Air Missile SAR Synthetic Aperture Radar. Transmits microwaves. SCS Soil Conservation Service SEAWIFS Sea-viewing Wide-Field of view Sensor SIPRNET Secret Internet Protocol Router Network SIR-A, B, C Shuttle Imaging Radar. U.S. Space Shuttle SPIN-2 Space Information-2 meter. Russian satellite imagery. SPOT Systeme Probatoire d’Observation de la Terre. French
multi-spectral satellite. STAR-3i Commercial Satellite Radar TBMCS Theater Battle Management Core System TERRA Latin for land. NASA’s Earth Observing System flagship satellite.
Multi-spectral. TIR Thermal Infrared µm Micrometer. Equal to 1 x 10 ^(-6) meter USAF United States Air Force
G-3
USAFA United States Air Force Academy USDA United States Department of Agriculture USDA-ASCS United States Department of Agriculture-Agricultural Stabilization
and Conservation Service USFS United States Forest Service USGS United States Geological Survey UV Ultraviolet UXO Unexploded Ordnance V Visible VFT Value-Focused Thinking WSR Weather Surveillance Radar
BIB-1
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imagery vs. aerial photography saving $120,000. n. pag. http://www.spot.com/home/APPLI/URBAN/martin/martin.htm. 01 Dec 2002.
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Frohn, Robert, C. Professor of Geography, University of Cincinnati, Cincinnati OH.
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VIT-1
Vita
Captain Steve J. Paylor graduated from Buckeye Central High School in New
Washington, Ohio. He attended Ohio State University in Columbus, Ohio where he
graduated with a Bachelor of Science degree in Civil Engineering with a specialization in
Remote Sensing in December 1995. He was commissioned through Officer Training
School (OTS), Maxwell AFB, Alabama, in May 1997. Following OTS, Captain Paylor
was assigned to Goodfellow AFB, Texas for intelligence career-field training. In April
1998, he attended the Army’s Defense Mapping School at Fort Belvoir, Virginia, to study
mapping, charting, and geodesy. In June 1998, he reported to the 27th Operational
Support Squadron at Cannon AFB, New Mexico, where he served as Chief of
Intelligence, Readiness. Captain Paylor also served as Chief of Operational Intelligence
at Cannon’s 523 Fighter Squadron.
Captain Paylor cross-trained into the Civil Engineering career-field while
stationed at Cannon AFB. In December 1999, he began service as a Civil Engineer for
the 27th Civil Engineer Squadron. He graduated from the Base Civil Engineer
Organization Course at the Air Force Institute of Technology (AFIT) in September 2000.
In December 2000, he served as Chief of SABER (Simplified Acquisition of Base
Engineering Requirements). Captain Paylor began his master’s studies in August 2001 at
the AFIT Graduate School of Engineering. Upon graduation, he will serve as Civil
Engineering Flight Commander, Logistics Support Squadron, 33rd Fighter Wing, Eglin
AFB, Florida.
REPORT DOCUMENTATION PAGE Form Approved OMB No. 074-0188
The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of the collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to an penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY)
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5a. CONTRACT NUMBER
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4. TITLE AND SUBTITLE REMOTE SENSING SYSTEMS OPTIMIZATION FOR GEOBASE ENHANCEMENT 5c. PROGRAM ELEMENT NUMBER
5d. PROJECT NUMBER If funded, enter ENR # 5e. TASK NUMBER
6. AUTHOR(S) Paylor, Steve, J., Captain, USAF 5f. WORK UNIT NUMBER
7. PERFORMING ORGANIZATION NAMES(S) AND ADDRESS(S) Air Force Institute of Technology Graduate School of Engineering and Management (AFIT/EN) 2950 P Street, Building 640 WPAFB OH 45433-7765
8. PERFORMING ORGANIZATION REPORT NUMBER AFIT/GEE/ENV/03-20
10. SPONSOR/MONITOR’S ACRONYM(S)
9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) AF/ILE Attn: Col. Brian J. Cullis JP-2 8th Floor Comm: 703-604-3468 Washington DC 20330 E-mail: [email protected]
11. SPONSOR/MONITOR’S REPORT NUMBER(S)
12. DISTRIBUTION/AVAILABILITY STATEMENT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.
13. SUPPLEMENTARY NOTES 14. ABSTRACT
The U.S. Air Force is implementing GeoBase, a geographic information system (GIS), throughout its worldwide installations. Air Force GIS needs can be augmented by aerial and satellite imagery. Imagery improvements over the past several years will significantly increase GeoBase usefulness in a range of mission requirements. Potential Air Force uses of imagery include identifying heat loss, environmental monitoring, command decision-making, and emergency response. A decision tool was developed to determine the appropriate imagery for Air Force Applications. Literature review and a 2002 Air Force Geo-Integration Office (AF/GIO) survey were used to develop a comprehensive imagery applications list that satisfies Air Force mission requirements. An imagery decision matrix allows users to select an application and find imagery that fulfills requirements. An imagery system key provides further details of each imagery type. Three Air Force bases validated the matrix. Increased awareness of an imagery-enriched GeoBase and efficiency afforded by the matrix, greatly reduces the time to identify and implement imagery. Available imagery was identified for the three Air Force bases at the National Imagery and Mapping Agency (NIMA) through a government contract at no additional cost. Current IKONOS imagery of Elmendorf Air Force base was obtained for analysis and implementation into GeoBase. 15. SUBJECT TERMS Remote Systems, Satellite Imagery, Aerial Imagery, GeoBase, Geographical Information Systems
16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON Heidi S. Brothers, Lt Col, USAF (ENV)
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